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Adolfsson, Alexander, and Daniel Arrhenius. "Overseeing Intersection System for Autonomous Vehicle Guidance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254219.
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Intersections represents one of the most common accident sites in traffic today. The biggest cause of accidents is obstructed view and subpar communication between vehicles. Since autonomous vehicles rely on sensors that require a direct view intersections are some of the most complex situations. Where the potential for inter vehicular communication exists between modern vehicles, it is absent in the older generation. An overseeing intersection system can fill this function during the transition period to fully autonomous traffic. This project aimed to implement an intersection system to assist autonomous vehicles through a crossroad. The assist system’s objective was to collect and transmit data from cars close to the junction to the autonomous vehicles nearby. The concept was tested in simulations by having models traverse a crossroad to evaluate how it utilised the external information. No persistent conclusion could be made due to insufficient simulation environment and vehicle model control.
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Bergquist, Urban. "Colour Vision and Hue for Autonomous Vehicle Guidance." Thesis, Linköping University, Linköping University, Computer Vision, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54362.
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<p>We explore the use of colour for interpretation of unstructured off-road scenes. The aim is to extract driveable areas for use in an autonomous off-road vehicle in real-time. The terrain is an unstructured tropical jungle area with vegetation, water and red mud roads.</p><p>We show that hue is both robust to changing lighting conditions and an important feature for correctly interpreting this type of scene. We believe that our method also can be deployed in other types of terrain, with minor changes, as long as the terrain is coloured and well saturated.</p><p>Only 2D information is processed at the moment, but we aim at extending the method to also treat 3D information, by the use of stereo vision or motion.</p>
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Naeem, Wasif. "Guidance and control of an autonomous underwater vehicle." Thesis, University of Plymouth, 2004. http://hdl.handle.net/10026.1/2822.
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A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the flight. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (CA) and fuzzy logic. A CA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The CA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a CA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.
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Mark, Wannes van der. "Stereo and colour vision techniques for autonomous vehicle guidance." [S.l : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2007. http://dare.uva.nl/document/47628.
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Magrino, Christopher. "Three dimensional guidance for the NPS autonomous underwater vehicle." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28610.
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Cloutier, Michael John. "Guidance and control system for an Autonomous Underwater Vehicle." Thesis, Monterey, California. Naval Postgraduate School, 1990. http://hdl.handle.net/10945/30635.
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Approved for public release, distribution is unlimited<br>The Naval Postgraduate School (NPS) is currently involved in a long-term project to investigate and develop real-time control software, artificial intelligence, computer architecture and control systems theory as they pertain to U.S. Navy autonomous vehicle programs. In support of this goal, the NPS is currently designing and fabricating a testbed autonomous underwater vehicle. This work describes the design, development, and testing of a Guidance Subsystem for this testbed vehicle which uses portions of cubic spirals as the desired path to follow between waypoints. In addition, data translation firmware and real-time software for the control surfaces and main motors is designed, implemented and tested. The process of selecting and implementing an appropriate computer architecture in support of these goals is also discussed and detailed, along with the choice of associated computer hardware and real-time operating system software.
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Pears, Nicholas Edwin. "The low-level guidance of an experimental autonomous vehicle." Thesis, Durham University, 1989. http://etheses.dur.ac.uk/6731/.
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This thesis describes the data processing and the control that constitutes a method of guidance for an autonomous guided vehicle (AGV) operating in a predefined and structured environment such as a warehouse or factory. A simple battery driven vehicle has been constructed which houses an MC68000 based microcomputer and a number of electronic interface cards. In order to provide a user interface, and in order to integrate the various aspects of the proposed guidance method, a modular software package has been developed. This, along with the research vehicle, has been used to support an experimental approach to the research. The vehicle's guidance method requires a series of concatenated curved and straight imaginary Unes to be passed to the vehicle as a representation of a planned path within its environment. Global position specifications for each line and the associated AGV direction and demand speed for each fine constitute commands which are queued and executed in sequence. In order to execute commands, the AGV is equipped with low level sensors (ultrasonic transducers and optical shaft encoders) which allow it to estimate and correct its global position continually. In addition to a queue of commands, the AGV also has a pre-programmed knowledge of the position of a number of correction boards within its environment. These are simply wooden boards approximately 25cm high and between 2 and 5 metres long with small protrusions ("notches") 4cm deep and 10cm long at regular (Im) intervals along its length. When the AGV passes such a correction board, it can measure its perpendicular distance and orientation relative to that board using two sets of its ultrasonic sensors, one set at the rear of the vehicle near to the drive wheels and one set at the front of the vehicle. Data collected as the vehicle moves parallel to a correction board is digitally filtered and subsequently a least squares line fitting procedure is adopted. As well as improving the reliability and accuracy of orientation and distance measurements relative to the board, this provides the basis for an algorithm with which to detect and measure the position of the protrusions on the correction board. Since measurements in three planar, local coordinates can be made (these are: x, the distance travelled parallel to a correction board; and y,the perpendicular distance relative to a correction board; and Ɵ, the clockwise planar orientation relative to the correction board), global position estimation can be corrected. When position corrections are made, it can be seen that they appear as step disturbances to the control system. This control system has been designed to allow the vehicle to move back onto its imaginary line after a position correction in a critically damped fashion and, in the steady state, to track both linear and curved command segments with minimum error.
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Boulekchour, M. "Robust convex optimisation techniques for autonomous vehicle vision-based navigation." Thesis, Cranfield University, 2015. http://dspace.lib.cranfield.ac.uk/handle/1826/9412.
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This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods.
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Jantapremjit, Pakpong. "A guidance-control approach applied to an autonomous underwater vehicle." Thesis, University of Southampton, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494914.
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Subramanian, Vijay. "Autonomous vehicle guidance using machine vision and laser radar for agricultural applications." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011323.
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Chen, Yuanyan. "Autonomous Unmanned Ground Vehicle (UGV) Follower Design." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1470951910.
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Recoskie, Steven. "Autonomous Hybrid Powered Long Ranged Airship for Surveillance and Guidance." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31711.
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With devastating natural disasters on the rise, technological improvements are needed in the field of search and rescue (SAR). Unmanned aerial vehicles (UAVs) would be ideal for the search function such that manned vehicles can be prioritized to distributing first-aid and ultimately saving lives. One of the major reasons that UAVs are under utilized in SAR is that they lack a long flight endurance which compromises their effectiveness. Dirigibles are well suited for SAR missions since they can hover and maintain lift without consuming energy and can be easily deflated for packaging and transportation. This research focuses on extending flight endurance of small-scale airship UAVs through improvements to the infrastructure design and flight trajectory planning.
In the first area, airship design methodologies are reviewed leading to the development and experimental testing two hybrid fuel-electric power plants. The prevailing hybrid power plant design consists of a 4-stroke 14cc gasoline engine in-line with a brushless DC motor/generator and variable pitch propeller. The results show that this design can produce enough mechanical and electrical power to support 72 hours of flight compared to 1-4 hours typical of purely electric designs. A power plant configuration comparison method was also developed to compare its performance and endurance to other power plant configurations that could be used in dirigible UAVs. Overall, the proposed hybrid power plant has a 600% increase in energy density over that of a purely electric configuration.
In the second area, a comprehensive multi-objective cost function is developed using spatially variable wind vector fields generated from computational fluid dynamic analysis on digital elevations maps. The cost function is optimized for time, energy and collision avoidance using a wavefront expansion approach to produce feasible trajectories that obey the differential constraints of the airship platform. The simulated trajectories including 1) variable vehicle velocity, 2) variable wind vector field (WVF) data, and 3) high grid resolutions were found to consume 50% less energy on average compared to planned trajectories not considering one of these three characteristics.
In its entirety, this research addresses current UAV flight endurance limitations and provides a novel UAV solution to SAR surveillance.
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Plew, Jason. "Development of a flight avionics system for an autonomous Micro Air Vehicle." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008540.
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Jourdan, Damien 1978. "Trajectory design and vehicle guidance for a mid-air rendezvous between tow autonomous aircraft." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/44514.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.<br>Page 126 blank.<br>Includes bibliographical references (p. 101-102).<br>The Parent Child Unmanned Air Vehicles (PCUAV) project is the fruit of a four year collaboration between M.I.T. and the Draper Lab. PCUAV aimed at providing close range observation from a distance using a low cost autonomous system. After defining the concept for the two first years, the PCUAV team focused on a key enabler of the system, the autonomous docking of two aircraft in mid-air. This thesis presents the work done by the author regarding the development of avionics by which the two aircraft can autonomously be guided within 15m, one behind the other. The key features needed to achieve this goal are discussed. First, the design of the trajectory, to be followed by the chasing aircraft, is presented. Then, several options for the guidance of the vehicles are explored and the adaptation of Proportional Navigation for PCUAV is discussed. Finally, the synchronization required to bring the two vehicles in trail and 15m from each other is explained. Flight test results validating this work are also presented.<br>by Damien Jourdan.<br>S.M.
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Lin, Letian. "Line-of-Sight Guidance for Wheeled Ground Vehicles." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1596630464772653.
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Haroz, Carla (Carla Shariz) 1974. "A predictor-corrector guidance algorithm design for a low L/D autonomous re-entry vehicle." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/31085.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 1999.<br>Includes bibliographical references (p. 171).<br>The Precision Landing Reusable Launch Vehicle (PL-RLV) is a low L/D, 2-stage craft with a mission plan that calls for low cost, speedy retrieval, and quick turn-around-times for successive flights. A guidance scheme that best adheres to these goals and captures the vehicle's capability is desired. During re-entry, the PL-RLV's second stage, the Precision Landing Vehicle 2 (PLV-2), will perform a reversal maneuver. This thesis concentrates on a possible re-entry guidance scheme for the PLV-2 during the terminal phase, the time from the completion of the reversal until the landing system parachutes are deployed. A simple bank-to-steer algorithm is suggested. The angle of attack is trimmed, and the bank angle (or bank rate) remains as the only means for control. The algorithm controls the time history of the vehicle's bank angle and tunes the bank angle history to meet landing and fuel requirements. This versatile guidance approach employs predictor-corrector methods. The guidance scheme presented generates a possibility of bankrate profiles within limitations that could be used for target acquisition. Selection of a robust target location and the nominal bankrate profile which will yield a minimum target miss are investigated. Testing shows the trade-offs between fuel cost and landing capability. Dispersion testing with winds and density are also performed. The predictor-corrector combination can yield target miss distances on the order of hundreds of feet or less. Open-loop and closed-loop results display the guidance system's ability to capture the PLV-2's capability in the presence of dispersions while still meeting system requirements.<br>by Carla Haroz.<br>S.M.
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MADASCHI, Matteo Giacomo. "Design and implementation of guidance control system for autonomous light electric vehicles." Doctoral thesis, Università degli studi di Bergamo, 2013. http://hdl.handle.net/10446/28965.
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In this work; a light AGV is presented. Light AGVs are usually designed to move small payloads and their distinctive feature is their high flexibility and re-configurability in the load handling. However; they have usually limited load weight capacity. In fact; large load weight values have a high impact on the guidance performances; even affecting the vehicle mechanical stability and strong speed limitations are usually applied.
We will focus our attention to the vehicle guidance control problem and aim to design controllers that guarantees desirable specifications for a wide range of possible load mass values (from 50kg to 1000kg). Hence; we assume that the payload mass is an uncertainty in the model and resort to robust control design methods. Moreover; we'd like not only to find a single suitable controller but to analyze the stability domain in the controller parameter space; so that we can evaluate the effects on the guidance performances produced by changes in the controller parameters; preserving control system stability. In order to obtain such results we will pay special attention to randomized techniques.
Randomized techniques are oriented to deal with basic notions for any engineering characteristics – gain or phase margin; overshoot or other time-response characteristics; robustness margin – as well as mathematical objectives such as H2 or Hinf norm.
Specifically; randomized method are applied to the tuning of PI controllers; taking into account control action limitations; due to the limited current supply values; system speed specifications; defined by desired values for settling time; Hinf performances.
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Miller, Erik. "Implementation of a Scale Semi-Autonomous Platoon to Test Control Theory Attacks." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2057.
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With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations.
For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control.
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Chomel, Christina T. (Christina Tvrdik) 1973. "Design of a robust integrated guidance and control algorithm for the landing of an autonomous reusable launch vehicle." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9940.
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Sattigeri, Ramachandra Jayant. "Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16272.
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The role of vision as an additional sensing mechanism has received a lot of attention in recent years in the context of autonomous flight applications. Modern Unmanned Aerial Vehicles (UAVs) are equipped with vision sensors because of their light-weight, low-cost characteristics and also their ability to provide a rich variety of information of the environment in which the UAVs are navigating in. The problem of vision based autonomous flight is very difficult and challenging since it requires bringing together concepts from image processing and computer vision, target tracking and state estimation, and flight guidance and control.
This thesis focuses on the adaptive state estimation, guidance and control problems involved in vision-based formation flight. Specifically, the thesis presents a composite adaptation approach to the partial state estimation of a class of nonlinear systems with unmodeled dynamics. In this approach, a linear time-varying Kalman filter is the nominal state estimator which is augmented by the output of an adaptive neural network (NN) that is trained with two error signals. The benefit of the proposed approach is in its faster and more accurate adaptation to the modeling errors over a conventional approach.
The thesis also presents two approaches to the design of adaptive guidance and control (G&C) laws for line-of-sight formation flight. In the first approach, the guidance and autopilot systems are designed separately and then combined together by assuming time-scale separation. The second approach is based on integrating the guidance and autopilot design process. The developed G&C laws using both approaches are adaptive to unmodeled leader aircraft acceleration and to own aircraft aerodynamic uncertainties.
The thesis also presents theoretical justification based on Lyapunov-like stability analysis for integrating the adaptive state estimation and adaptive G&C designs. All the developed designs are validated in nonlinear, 6DOF fixed-wing aircraft simulations.
Finally, the thesis presents a decentralized coordination strategy for vision-based multiple-aircraft formation control. In this approach, each aircraft in formation regulates range from up to two nearest neighboring aircraft while simultaneously tracking nominal desired trajectories common to all aircraft and avoiding static obstacles.
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Aribal, Seckin. "Development Of An Autopilot For Automatic Landing Of An Unmanned Aerial Vehicle." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613391/index.pdf.
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This thesis presents the design of an autopilot and guidance system for an unmanned aerial vehicle. Classical (PID) and modern control (LQT, Sliding Mode) methods for autonomous navigation and landing in adverse weather conditions are implemented. Two different guidance systems are designed in order to navigate through waypoints during normal and/or emergency flight. The nonlinear Pioneer UAV model is used in controller development and simulations.
Aircraft is linearized at different trim points and total airspeed, altitude, roll and yaw autopilots are designed using Matlab/Simulink environment for lateral and longitudinal control of the aircraft. Gain scheduling is used to combine controllers designed for different trim points. An optimal landing trajectory is determined using &ldquo<br>Steepest Descent&rdquo<br>Algorithm according to the dynamic characteristics of the aircraft. Optimal altitude trajectory is used together with a lateral guidance against cross-wind disturbance.
Finally, simulations including landing under crosswind, tailwind, etc., are run and the results are analyzed in order to demonstrate the performance and effectiveness of the controllers.
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Hernàndez, Bes Emili. "Path planning with homotopic constraints for autonomous underwater vehicles." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/83568.
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This thesis addresses the path planning problem for Autonomous Underwater Vehicles (AUVs) using homotopy classes to provide topological information on how paths avoid obstacles. Looking for a path within a homotopy class constrains the search into a specific area of the search space, speeding up the computation of the path. Given a workspace with obstacles, the method starts by generating the homotopy classes. Those which probably contain lower cost solutions are determined by means of a lower bound criterion before computing a path. Finally, a path planner uses the topological information of homotopy classes to generate a few good solutions very quickly. Three path planners from different approaches have been proposed to generate paths for the homotopy classes obtained. The path planning is performed on Occupancy Grid Maps (OGMs) improved with probabilistic scan matching techniques. The results obtained with synthetic s scenarios and with real datasets show the feasibility of our method.<br>Aquesta tesi aborda el problema de la planificació de camins per a Vehicles Submarins Autònoms (AUVs) mitjançant la utilització de classes d'homotopia per a proporcionar informació topològica de com els camins eviten els obstacles. Calcular un camí dins d'una classe d'homotopia permet limitar l'espai de cerca accelerant-ne el càlcul de la solució. Donat un workspace amb obstacles, el mètode primer genera les classes homotòpiques. Aquelles classes que probablement contenen les solucions de menor cost s'identifiquen per mitjà d'una heurística sense haver-ne de calcular el camí al workspace. Finalment, un planificador de camins utilitza la informació topològica de les classes d'homotopia per generar solucions segons les classes seleccionades molt ràpidament. El mètode de planificació de camins s’aplica sobre Mapes d’Occupació de Graella (OGMs) millorats amb tècniques de scan matching probabilístic. Els tests i resultats obtinguts tan en escenaris sintètics com en datasets reals mostren la viabilitat del nostre mètode.
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Adler, Benjamin [Verfasser], and Jianwei [Akademischer Betreuer] Zhang. "System Design and Real-Time Guidance of an Unmanned Aerial Vehicle for Autonomous Exploration of Outdoor Environments / Benjamin Adler. Betreuer: Jianwei Zhang." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2015. http://d-nb.info/1068931221/34.
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Prince, Robert A. "Autonomous visual tracking of stationary targets using small unmanned aerial vehicles." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FPrince.pdf.
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恭史, 山田, and Yasufumi Yamada. "A study on cheap robust sensing for obstacle avoidance guidance based on bio-sonar strategy of bats." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13045017/?lang=0, 2017. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13045017/?lang=0.
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コウモリは1送信2受信器のミニマルな超音波センシングデザインからは想像できない，高度な3次元飛行を実現させている．本論文では，①繰り返し同じ障害物環境下を飛行するコウモリの未知と既知の空間に対する音響センシング行動の違いを比較した．さらに，②未知環境飛行時に見られる特徴的な空間スキャニングの行動パターンをモデル化し，自律走行車を用いてコウモリの行動の有用性を実環境センシングのふるまいから定量的に評価した．<br>Bats possess a highly developed biosonar system that can be regarded as the minimum sensor requirement for three-dimensional spatial sensing. The present study 1) experimentally investigated changes in the pulse direction, pulse emission timing and flight path of CF-FM bats during an obstacle avoidance flight as the bats became familiar with the space around them and 2) expressed behavioral principles observed in the bats during flight, especially in an unfamiliar space, using an algorithm and then embedded the principles into an autonomous vehicle equipped with simple ultrasound sensors. The findings of this world-leading biomimetic research offer new possibilities for artificial-intelligence navigation systems.<br>博士(工学)<br>Doctor of Philosophy in Engineering<br>同志社大学<br>Doshisha University
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Wieczorek, Italo de Avila. "A power line detection algorithm to support a fine grain UAV movement guidance." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163770.
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Detecção de linhas de alta tensão em ambientes complexos é uma das tarefas mais desafiadoras em inspeções que utilizam Veículos Aéreos Não Tripulados (VANTs). Este trabalho foca em dar uma solução para este desafio, através do desenvolvimento de um algoritmo de controle de voo de precisão, que guie o VANT de maneira autônoma sobre as linhas de alta tensão. O algoritmo proposto é baseado em quatro etapas: Captura da Imagem, Filtragem da Imagem, Detecção das Linhas e Controle de Voo. Inicialmente a imagem é redimensionada para um tamanho em que as linhas fiquem em maior evidência, depois uma sequência de filtros é aplicada na imagem para reduzir ruído e evidenciar ainda mais as linhas. Depois deste pré-tratamento, um filtro de duas dimensões com formato similar ao de uma linha de alta tensão é usado para extrair os pixels pertencentes as bordas destas linhas. Após a aplicação do filtro de duas dimensões, a Transformada de Hough é aplicada na imagem resultante para detectar os segmentos de reta. Por fim, todos os dados obtidos no processamento da imagem são utilizados para guiar o VANT de maneira autônoma pelas linhas de transmissão. O algoritmo proposto apresenta um eficiente sistema de detecção de linhas de alta tensão, para auxiliar o controle de voo autônomo de um VANT, apresentando resultados convincentes.<br>Power lines detection in complex environments is one of the most important and challenging tasks in Unmanned Aerial Vehicles (UAV)-based inspections. This work focuses on tackling this challenge by developing a control algorithm to support fine grain UAV control to autonomously guide the aerial platform over the power lines. The proposed algorithm is based on four stages: Image Capturing, Image Filtering, Line Detection and Flight Control. Firstly, the image is cropped to a size that fits all the power lines, then a sequence of filters is applied in the image to reduce noise and highlight these lines. After all the image's pretreatment, a 2D filter with similar shape of a power transmission line is used to extract pixels that belongs to the line's edges. Then, the Hough Transform method detects the line segments in the edges result image. Lastly all the obtained data is used to autonomously guide a UAV over the power transmission lines. The proposed algorithm presents an efficient power transmission lines detecting system to support the autonomous UAV guidance, which presents convincing results.
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McDowell, Journey. "Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2100.
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Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research.
Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case.
In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn't the trailer it trained on.
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Lekkas, Anastasios M. "Guidance and Path-Planning Systems for Autonomous Vehicles." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-24998.
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This thesis is concerned with two interconnected and very important problems regarding the autonomy of vehicles, namely, path planning and guidance. By adopting a modular approach, path planning and guidance can be viewed as two modules which belong to a wider context consisting of four modules, the other two being navigation and control. All four modules interact with each other and none is completely independent. Path planning deals with what we want to achieve (by defining spatial and temporal constraints), and guidance dictates how we should act in order to achieve it (by generating appropriate reference trajectories to be fed to the corresponding controllers). Therefore it is important to develop: a) path design methodologies, which will generate feasible and safe paths with several desired properties, and b) guidance laws capable of generating reference trajectories which will lead the vehicle on the desired path, even when unknown disturbances (such as ocean currents and wind forces) affect the vehicle’s motion. Four contributions pertaining to the path-planning problem are included in this thesis. The most important is the use of Fermat’s spiral (FS) as an alternative to both Dubins paths and clothoids. We show that paths consisting of straight lines and FS arcs are curvature-continuous, computationally inexpensive and can be used for path tracking by changing the parametrization. The second contribution is the development of a number of path-evaluation criteria which aim at providing an onboard computer with sufficient information for selecting the right path for a given application. The methodology is still at its infancy but several improvements, which could result in fast progress, are discussed. The third contribution is the use of a monotone cubic Hermite spline for path-planning purposes. The main advantage is that the method generates very practical paths which do not include wiggles and zig zags between two successive waypoints. Moreover, the method provides the user with better shape control, a property which can turn out to be very useful in real-time collision-avoidance applications. The fourth contribution pertains to a collision-avoidance strategy combining the Voronoi diagrams (VD) method and FS-based path generation. An intuitive and efficient procedure is developed for obtaining smooth paths which keep the vehicle at a safe distance from all obstacles on the map and at the same time avoid unnecessary heading changes. The thesis also presents a number of guidance-related contributions, each of varying degree of importance and difficulty. The first one is the modification of the line-of-sight (LOS) guidance by introducing a time-varying equation for the lookahead distance . This aims at obtaining a more flexible behavior regarding the steering of the vehicle because for very small the vehicle approaches the target path at a direction almost normal to the path, whereas for very large it takes a longer time for the vehicle to converge to the path. The effect of the time-varying equation from a stability viewpoint is investigated. The second (and minor) contribution is the consideration of a 5-DOF vehicle kinematic model (common for torpedo-shaped underwater vehicles which do not control the roll angle) and the influence of the coupling between the horizontal and vertical planes on the expression for the sideslip angle. This led to the third contribution, which is a transformation of the LOS guidance in quaternion form for both the uncoupled and the coupled cases. The transformation is based on exploiting very simple trigonometric properties and the geometry of the LOS guidance. The fourth contribution is an integral LOS guidance law capable of eliminating the errors induced by constant external disturbances. The method is formulated using absolute velocity-based vehicle kinematics and simple Lyapunov-based analysis. The fifth contribution moves a few steps further and presents two adaptive integral LOS guidance laws which compensate for the errors induce by ocean currents. These methods are based on the vehicle kinematics in relative-velocity form. This is a very useful result for underwater vehicles, where absolute velocity measurements might not be available. The effect of the current on the direction normal to the direction of motion (that is, the force inducing the cross-track error) is estimated, and stability results for curved paths are also given. The sixth contribution is the development of a guidance technique where, in addition to the LOS guidance for minimizing the cross-track error, surge velocity commands are generated as well in order to minimize the along-track error, hence satisfying constraints related to the path-tracking (or trajectory-tracking) motion control scenario. Finally, the path-tracking solution is combined with the indirect adaptive integral LOS so as to achieve path tracking under the influence of ocean currents, which also results in estimating all the parameters of the current (that is, current velocity and orientation w.r.t. the inertial frame). In all cases, particular emphasis was placed on finding solutions that are simple and, at the same time, efficient.
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Tanakitkorn, Kantapon. "Guidance, control and path planning for autonomous underwater vehicles." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/412635/.
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Over-actuated, hover-capable autonomous underwater vehicles (AUVs) are hybrid vehicles designed to serve a wide range of operations, from detailed survey tasks in a hover style to long-range exploration tasks in a flight style. Due to the over-actuated design and the different operating styles, it is challenging to operate these types of AUVs. This research developed a navigation system for over-actuated, hover-capable AUVs, with a primary focus on interconnections between guidance, control and path planning systems. A detailed 6-degrees of freedom mathematical model was developed to represent dynamics of these hybrid AUVs. The model was validated with horizontal plane manoeuvring trials carried out on the over-actuated, hover-capable Delphin2 AUV. The results showed that the model can satisfactorily predict the AUV's response over a range of operating conditions. Using this dynamics model, the guidance and control systems were designed to effectively and efficiently operate over a range of forward speeds with a seamless transition between hover-style and flight-style operation. A path planning system was tested, seeking for the collision-free path between two locations that requires the least energy for an AUV to navigate along. The proposed navigation system has been verified on the Delphin2 AUV through experiments. The comprehensive results have shown that the system can maintain excellent performance regardless of a range of forward speeds. By applying the proposed navigation system, it is also possible to launch a long-endurance AUV from a shore. The vehicle would be able to automatically navigate along an energy efficient path to perform, for instance, a seabed survey mission at a remote part of the ocean, then come back to the recovery point. This concept eliminates a ship from the AUV launching process, hence, making the operation more cost-effective.
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Bouzid, Yasser. "Guidance and control system for autonomous aerial vehicles navigation." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLE014.
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Cette thèse traite du guidage et du pilotage de véhicules aériens qui peuvent assurer des missions dans des lieux particulièrement hostiles, dangereux ou inaccessibles avec des véhicules conventionnels. Nous sommes tout d'abord motivés par le scénario de couverture, qui est généralement un processus long pouvant utiliser un grand nombre de personnes et d'équipements. Or, la nature de la couverture nécessite un véhicule aérien avec des capacités de vol stationnaire. Pour cela, nous nous intéressons alors aux multirotors, qui sont considérés comme une bonne étude de cas pour concevoir, analyser et mettre en œuvre des stratégies de contrôle de vol.En réalité, de nombreux défis sont encore ouverts pour ce qui concerne le scénario de couverture comme la faisabilité, l’optimalité en visitant tous les points d’intérêts. De plus, un système de contrôle robuste est indispensable pour contrer des effets néfastes tel le vent. Par ailleurs, la conception d'un algorithme de contrôle répondant à certaines exigences (structure simple, précision, énergie minimale consommée) constitue un défi supplémentaire. Ensuite, notre travail introduit un modèle mathématique générique pour les multi-rotors en considérant l’effet du vent.Dans la première partie du manuscrit, nous proposons des planificateurs en utilisant comme base l'algorithme RRT* (optimal Rapidly-exploring Random Tree). En fait, dans les grands espaces, un grand nombre de nœuds est généré augmentant alors le temps de calcul et la mémoire consommée. Pour y remédier, une procédure de suppression est impliquée pendant le processus « ReWire » pour les réduire. De plus, un planificateur multidirectionnel qui renvoie un ensemble de chemins optimaux à partir d'un point de départ et d'un ensemble de points objectifs est proposé. Notre travail introduit également une stratégie CPP (Coverage path-planning) optimale dans un espace contraint. Celle-ci consiste à procéder par un algorithme en deux phases. Dans la première, un planificateur multidirectionnel est utilisé pour définir les chemins les plus courts de chaque point à ses voisins. Dans la seconde phase, au moyen des coûts entre les points, le chemin global le plus court est obtenu en résolvant un problème de voyageur en utilisant des algorithmes génétiques. Puis, compte tenu de l'énergie embarquée limitée, un problème de routage est adapté et est résolu par la méthode de savings. Dans une seconde partie, nous nous sommes penchés sur la conception d'un système de pilotage efficace permettant au véhicule de suivre une trajectoire paramétrée dans le temps. D’une part nous proposons une extension de la commande par modèle interne au non-linéaire (NLIMC). Notre technique repose sur l’utilisation du principe de base IMC pour synthétiser un contrôleur non linéaire qui fait intervenir la propriété de platitude. D’autre part, nous proposons une autre forme de contrôleur dont la structure apparente est un PID mais dans lequel est incorporée la technique des modes glissants que l'on appellera aussi PID non linéaire bien qu’il diffère de l’existant. Cette combinaison a l’avantage de conduire à un bon niveau de robustesse fourni par les modes glissants et en même temps à un bon comportement spécifié par la structure PID. En outre, en guise de complément, nous proposons deux contrôleurs redondants basés sur deux principes distincts afin de booster et d’améliorer les capacités de tout contrôleur. Le premier est basé sur l’approche MFC (Model-Free Control) tandis que le second est basé sur les modes glissants dynamiques DSMC (Dynamic Sliding Mode Controller). Enfin, pour montrer les performances de ces contrôleurs, nous avons effectué une série de tests avec plusieurs illustrations et scénarios, nous avons dressé un tableau de comparaison avec les approches conventionnelles. Les résultats issus des simulations numériques et ceux des tests expérimentaux réalisés sur un drone quadrotor se sont avérés cohérents et semblent bien prometteurs<br>This thesis deals with the guidance and control of aerial vehicles, which can also ensure missions in hostile, dangerous environments, or inaccessible workspaces with conventional vehicles. First, we are motivated by the coverage scenario, which is in general a long process, requiring a large number of individuals and specific equipment. However, the nature of sensing coverage requires an aerial vehicle with hovering capabilities. For this purpose, we are interested in multirotors that are considered as a good case study to design, analyze and implement flight control strategies.As matter of fact, many challenges are still open with respect to the coverage scenario such as for instance the feasibility and the optimality when passing through the Points of Interest. In addition, a robust control system is essential to mitigate the adverse effects such as the wind. Moreover, designing a control algorithm, which meet some requirements (simplicity, accuracy, consumed energy, etc.) constitutes a complementary challenge. Then, our work introduces a generic mathematical model for multirotors flying under the effect of wind.In a first part, we propose planners using as a basis the optimal Rapidly-exploring Random Tree (RRT*) algorithm. In fact, in large workspaces, a large number of nodes is generated and then increasing the computation time and the consumed memory. To counter these latter, a removal procedure is involved during the rewiring process. In addition, a multidirectional planner that returns a set of optimal paths from a starting point and a set of objective points is proposed. Our work also introduces an optimal Coverage path-planning (CPP) strategy in a constrained workspace. This one proceeds through a two-phases algorithm. In the first one, a Connected Multi-directional planner is used to define the shortest paths from each point to its neighbors. In the second phase, by means of the pair-wise costs between points, the overall shortest path is obtained by solving a Traveling Salesman Problem using Genetic Algorithms. Then, taking into account the limited on-board energy, a Capacitated-Vehicle Routing Problem is adapted and solved by the savings approach.In a second part, we study the design of an effective control system allowing the vehicle to track a trajectory parameterized in time. On the one hand, we propose an extension to nonlinear systems of the Internal Model Control (NLIMC). Our technique is based on the use of the basic IMC principle to synthesize a nonlinear controller that involves the property of flatness. On the other hand, we propose another form of controller whose apparent structure is a PID but in which the technique of sliding modes is incorporated that will also call the nonlinear PID (NLPID). This combination has the advantage to lead to a good level of robustness provided by the sliding modes and at the same time to a good behavior specified by the PID structure. Besides, as a complement, we present two redundant controllers based on two distinct principles in order to boost and to improve the capabilities of any controller. The first one is based on the Model-Free Control (MFC) approach while the second one is based on Dynamic Sliding Mode Controller (DSMC).Finally, to highlight the performance of these controllers, we have performed a series of tests with several illustrations and scenarios and we have drawn up a comparison table with conventional approaches. The results of both the numerical simulations and the experimentation that are performed on a quadrotor are consistent and seem to be quite promising
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Bertin, Étienne. "Robust optimal control for the guidance of autonomous vehicles." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAE012.
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Le guidage d'un lanceur réutilisable est un problème de contrôle qui nécessite à la fois précision et robustesse : il faut calculer une trajectoire et un contrôle, de sorte que le lanceur atteigne la piste d'atterrissage, sans s'écraser ni exploser en vol, le tout en utilisant le moins de carburant possible.Les méthodes de Contrôle Optimal issu du Principe de Pontryagin calculent une trajectoire optimale avec grande précision, mais les incertitudes, soit les erreurs entre les estimations de l'état initial et des paramètres et leurs valeurs réelles, causent une déviation potentiellement dangereuse de la trajectoire réelle. En parallèle, les méthodes ensemblistes et notamment la simulation validée peuvent encadrer toutes les trajectoires possibles d'un système dynamique avec des incertitudes bornées.Cette thèse combine ces deux approches pour encadrer des ensembles de trajectoires optimales de systèmes avec incertitudes afin de garantir la robustesse du guidage d'un véhicule autonome.Nous commençons par définir des ensembles de trajectoires optimales pour des systèmes avec incertitudes, d'abord pour les trajectoires mathématiquement parfaites, puis pour les trajectoires d'un véhicule sujet à des erreurs d'estimation, mais qui utiliserait, ou non, les données des capteurs pour recalculer sa trajectoire en cours de route. Le principe de Pontryagin caractérise ces ensembles comme solutions de problèmes aux deux bouts avec des dynamiques avec incertitudes. Nous développons alors des algorithmes qui encadrent toutes les solutions de ces problèmes aux deux bouts en utilisant la simulation validée, l'arithmétique des intervalles et la théorie des contracteurs. Cependant, la simulation avec des intervalles occasionne une forte sur-approximation qui limite nos méthodes. Pour y remédier, nous remplaçons les intervalles par des zonotopes symboliques contraints. Nous utilisons notamment ces zonotopes pour simuler des systèmes hybrides, encadrer des solutions de problèmes aux deux bouts et construire des sous-approximations en complément de la sur-approximation classique. Enfin, nous combinons tout ceci pour calculer des ensembles de trajectoires de systèmes aérospatiaux et les utilisons pour évaluer la robustesse du contrôle<br>The guidance of a reusable launcher is a control problem that requires both precision and robustness: one must compute a trajectory and a control such that the system reaches the landing zone, without crashing into it or exploding mid-flight, all while using as little fuel as possible. Optimal control methods based on Pontryagin's Maximum Principle can compute an optimal trajectory with great precision, but uncertainties, the discrepancies between estimated values of the initial state and parameters and actual values, cause the actual trajectory to deviate, which can be dangerous. In parallel, set-based methods and notably validated simulation can enclose all trajectories of a system with uncertainties.This thesis combines those two approaches to enclose sets of optimal trajectories of a problem with uncertainties to guarantee the robustness of the guidance of autonomous vehicles.We start by defining sets of optimal trajectories for systems with uncertainties, first for mathematically perfect trajectories, then for the trajectory of a vehicle subject to estimation errors that can use, or not use, sensor information to compute a new trajectory online. Pontryagin's principle characterizes those sets as solutions of a boundary value problem with dynamics subject to uncertainties. We develop algorithms that enclose all solutions of these boundary value problem using validated simulation, interval arithmetic and contractor theory. However, validated simulation with intervals is subject to significant over-approximation that limits our methods. To remedy that we replace intervals by constrained symbolic zonotopes. We use those zonotopes to simulate hybrid systems, enclose the solutions of boundary value problems and build an inner-approximation to complement the classical outer-approximation. Finally, we combine all our methods to compute sets of trajectories for aerospace systems and use those sets to assess the robustness of a control
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Tracy, Chisholm C. (Chisholm Cain) 1975. "Integrated entry guidance and control for autonomous reusable launch vehicles." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/8790.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1999.<br>Includes bibliographical references (p. 163-164).<br>The guidance and control approach employed on current reusable launch vehicles is based on classical control techniques developed for the Space Shuttle more than 20 years ago. This approach partitions the guidance and control tasks into separate loops resulting in a complex control architecture that can be difficult to adapt to new vehicle designs. A new entry guidance and control technique based on time-invariant linear quadratic regulator theory is presented. This approach integrates the guidance and control functions into a single multivatiable control loop that theoretically should yield improved performance over classical designs and greatly simplify the control architecture. This research is part of a larger effort to develop a next generation guidance and control system that can be implemented onboard the vehicle to increase robustness to unexpected flight conditions and abort scenarios. The integrated guidance and control algorithm is implemented in a nonlinear simulation of the Orbital Sciences X-34, a testbed for reusable launch vehicle technology. The performance and robustness of the linear quadratic regulator algorithm is compared to the classical guidance and control system developed by Orbital Sciences. The results indicate that the overall performance and robustness potential of the integrated guidance and control technique is similar to that of the classical approach. The integration of guidance, control, lateral, and longitudinal dynamics is not observed to yield significant performance improvements under the conditions tested. However, the integrated approach provides an equally effective, simplified control architecture that could allow onboard calculation of control gains in the future to yield a more robust system.<br>by Chisholm C. Tracy.<br>S.M.
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Kapelios, Ioannis. "Stability of turning rate guidance and control laws for autonomous vehicles." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23776.
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Giubilato, Riccardo. "Stereo and Monocular Vision Guidance for Autonomous Aerial and Ground Vehicles." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3422709.
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Robotic agents vastly increase the return of planetary exploration missions thanks to their ability of performing in-situ measurements. To this date, unmanned exploration has been performed by individual of robots such as the MER Spirit and Opportunity and later MSL Curiosity.
A fundamental asset to robotic autonomy is the ability to perceive the surroundings through vision systems such as stereo cameras. Since global localization using GPS-like approaches is unavailable in extra-terrestrial environments, rovers need to measure their motion in order to understand where they are heading. This allows to close high-level control loops to follow planned routes toward goals of scientific interest. Visual SLAM (Simultaneous Localization and Mapping) is an effective strategy to fulfill these needs. Stereo cameras are used to both reconstruct the environment structure through triangulation and use that information to localize the cameras while moving. While performing Visual SLAM on constrained resources is still challenging, many state of the art solution exist to solve this problem for single exploration sessions.
The future of planetary exploration however strongly involves cooperation amongst teams of heterogeneous robotic agents. While the SLAM problem is efficiently solved for single sessions and agents, robust solutions for collaborative map merging and re-localization are still topics of active research and constitute the first major objective of this thesis. Here is proposed and validated a robust re-localization pipeline targeted at planetary vehicles equipped with stereo vision systems allowing to localize them in previously built maps. Instead of common Visual SLAM approaches based exclusively on visual features, this algorithm exploits the invariant nature of 3D point clouds by using compact 3D binary descriptors in conjunction with texture cues. Maps are discretized in submaps which are represented in a lightweight form using the Bag of Binary Words paradigm. The algorithm is then tested and validated both in the laboratories of the DLR Robotics and Mechatronics Center and in Mount Etna, Sicily, an outdoor planetary analogous environment.
The second major research objective involves monocular vision for UAVs. Stereo depth perception is often infeasible for UAVs as small baseline systems degenerate to monocular as the vehicle takes off. 3D structure can be obtained using Structure-from-Motion approaches which are however unable to recover a global metric scale. Scale is traditionally recovered integrating accelerations from IMUs. However visual-inertial sensing is delicate being very sensitive on wrong extrinsic calibration. In addition, initialization of the visual-inertial pipeline is challenging and can diverge. These reasons challenge the implementation of unsupervised autonomous behaviors on UAVs. To address these issues, this thesis work proposes a sensor fusion approach between cameras and low resolution range sensors in order to exploit direct range measurements enforcing scale constraints in monocular Visual Odometry. This research objective is accomplished in two stages. Firstly a monocular Visual Odometry is developed without enforcing strict performance constraints and is used in conjunction with a low resolution Time of Flight camera, a lightweight sensor capable of measuring 64 ranges in a narrow Field-of-View. The algorithm is tested against both a state of the art stereo visual SLAM system and a more accurate, while heavier, 2D LiDAR. Finally, a real-time monocular Visual Odometry is developed exploiting a multi-threaded architecture to enable concurrent tracking of the camera pose and scale optimization in the background. This algorithm is tested with a 1D LiDAR altimeter, a minimal range sensing configuration of just 1 point per measurement, demonstrating the ability of recovering and maintain a correct scale along the trajectory with very light and inexpensive off-the-shelf range sensors.
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Petrich, Jan. "Improved Guidance, Navigation, and Control for Autonomous Underwater Vehicles: Theory and Experiment." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/27222.
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This dissertation addresses attitude control and inertial navigation of autonomous underwater vehicles (AUVs). We present theoretical justification for using simplified models, derive system identification algorithms, and verify our results through extensive field trials. Although this research focuses on small AUVs with limited instrumentation, the results are useful for underwater vehicles of any size.
For attitude control of aircraft systems, second-order equivalent pitch-axis models are common and extensively studied. However, similar analysis has not been performed for the pitch-axis motion of underwater vehicles. In this dissertation, we study the utility and the limitations of second-order approximate models for AUVs. We seek to improve the flight performance and shorten the time required to re-design a control algorithm when the shape, mass-distribution, and/or net buoyancy of an AUV/payload configuration changes.
In comparison to commonly implemented AUV attitude controllers, which neglect roll motion and address pitch and yaw dynamics separately, we derive a novel linear time-varying model that explicitly displays the coupling between pitch and yaw motion due to nonzero roll angle and/or roll rate. The model facilitates an Hâ control design approach that explicitly addresses robustness against those coupling terms and significantly reduces the effect of pitch and yaw coupling.
To improve AUV navigation, we investigate algorithms for calibrating a triaxial gyroscope using angular orientation measurements and formally define AUV trajectories that are persistently exciting and for which the calibration coefficients are uniformly observable. To improve AUV guidance, we propose a near real-time ocean current identification method that estimates a non-uniform flow-field using only sparse flow measurements.<br>Ph. D.
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Erekson, Ishmaal T. "Modified Trajectory Shaping Guidance for Autonomous Path Following Control of Platooning Ground Vehicles". DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4919.
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This thesis proposes a modification of trajectory shaping guidance to provide more accurate path convergence in curved paths. The object of this thesis is to apply this simple guidance law to platooning control to ensure all vehicles in the platoon converge to a desired constant radius path at a desired vehicle separation distance. To show the viability of this new guidance law, it is shown mathematically to be exponentially stable. It is also confirmed through simulations and on ground robots.
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Banks, Alec. "A nature inspired guidance system for unmanned autonomous vehicles employed in a search role." Thesis, Bournemouth University, 2009. http://eprints.bournemouth.ac.uk/15906/.
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Since the very earliest days of the human race, people have been studying animal behaviours. In those early times, being able to predict animal behaviour gave hunters the advantages required for success. Then, as societies began to develop this gave way, to an extent, to agriculture and early studies, much of it trial and error, enabled farmers to successfully breed and raise livestock to feed an ever growing population. Following the advent of scientific endeavour, more rigorous academic research has taken human understanding of the natural world to much greater depth. In recent years, some of this understanding has been applied to the field of computing, creating the more specialised field of natural computing. In this arena, a considerable amount of research has been undertaken to exploit the analogy between, say, searching a given problem space for an optimal solution and the natural process of foraging for food. Such analogies have led to useful solutions in areas such as numerical optimisation and communication network management, prominent examples being ant colony systems and particle swarm optimisation; however, these solutions often rely on well-defined fitness landscapes that may not always be available. One practical application of natural computing may be to create behaviours for the control of autonomous vehicles that would utilise the findings of ethological research, identifying the natural world behaviours that have evolved over millennia to surmount many of the problems that autonomous vehicles find difficult; for example, long range underwater navigation or obstacle avoidance in fast moving environments. This thesis provides an exploratory investigation into the use of natural search strategies for improving the performance of autonomous vehicles operating in a search role. It begins with a survey of related work, including recent developments in autonomous vehicles and a ground breaking study of behaviours observed within the natural world that highlights general cooperative group behaviours, search strategies and communication methods that might be useful within a wider computing context beyond optimisation, where the information may be sparse but new paradigms could be developed that capitalise on research into biological systems that have developed over millennia within the natural world. Following this, using a 2-dimensional model, novel research is reported that explores whether autonomous vehicle search can be enhanced by applying natural search behaviours for a variety of search targets. Having identified useful search behaviours for detecting targets, it then considers scenarios where detection is lost and whether natural strategies for re-detection can improve overall systemic performance in search applications. Analysis of empirical results indicate that search strategies exploiting behaviours found in nature can improve performance over random search and commonly applied systematic searches, such as grids and spirals, across a variety of relative target speeds, from static targets to twice the speed of the searching vehicles, and against various target movement types such as deterministic movement, random walks and other nature inspired movement. It was found that strategies were most successful under similar target-vehicle relationships as were identified in nature. Experiments with target occlusion also reveal that natural reacquisition strategies could improve the probability oftarget redetection.
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Pham, Ngoc Hai. "A Comprehensive Architecture for the Cooperative Guidance and Control of Autonomous Ground and Air Vehicles." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/1637.
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This thesis deals with the problem of cooperative explorations of a group of autonomous vehicles in unknown environments in the context of decentralized behaviour. The main contribution of this thesis is the development of a comprehensive decentralized cooperative exploration frame work in which each individual vehicle has the ability to explore an unknown environment by itself and also by cooperative behaviour in a team of several vehicles. To simulate the whole system, each individual vehicle will have the ability to explore an unknown environment by dynamically path-planning (with obstacle and collision avoidance), high-level con- trolling, updating the environment map, proposing potential destinations (frontiers), and solving online task assignment. In this thesis, the framework simulates an unknown environment as an occupancy grid map and uses a frontier-base exploration strategy, in which a cell will be marked as a frontier if it is adjacent at least one open cell, as the core architecture. In dealing with the uncertainties in process transition and observation models of autonomous vehicles, the well-known discrete extended Kalman filter (EKF) algorithm is investigated and implemented. When exploring the environment, a vehicle will update its surrounding information, then propose its potential destinations and evaluate the utility (benefit) to travel to each of those destinations. The benefit to go to each destination is derived from the subtraction of the utility (value) of that cell to the sum of the cost to travel to that cell and the steering cost. The key to cooperative exploration in the team of vehicles lies in each vehicle's ability to communicate the updates of the world to the whole team and to contribute to the global list of potential destinations. And each vehicle has the capability of solving the task assignment problem for the team by calling its own online-task-assignment solving engine. This algorithm results each vehicle in having a destination to visit, which benefits the whole team the most and reduces the total exploration time of the team.
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Pham, Ngoc Hai. "A Comprehensive Architecture for the Cooperative Guidance and Control of Autonomous Ground and Air Vehicles." University of Sydney, 2007. http://hdl.handle.net/2123/1637.
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Master of Engineering (Research)<br>This thesis deals with the problem of cooperative explorations of a group of autonomous vehicles in unknown environments in the context of decentralized behaviour. The main contribution of this thesis is the development of a comprehensive decentralized cooperative exploration frame work in which each individual vehicle has the ability to explore an unknown environment by itself and also by cooperative behaviour in a team of several vehicles. To simulate the whole system, each individual vehicle will have the ability to explore an unknown environment by dynamically path-planning (with obstacle and collision avoidance), high-level con- trolling, updating the environment map, proposing potential destinations (frontiers), and solving online task assignment. In this thesis, the framework simulates an unknown environment as an occupancy grid map and uses a frontier-base exploration strategy, in which a cell will be marked as a frontier if it is adjacent at least one open cell, as the core architecture. In dealing with the uncertainties in process transition and observation models of autonomous vehicles, the well-known discrete extended Kalman filter (EKF) algorithm is investigated and implemented. When exploring the environment, a vehicle will update its surrounding information, then propose its potential destinations and evaluate the utility (benefit) to travel to each of those destinations. The benefit to go to each destination is derived from the subtraction of the utility (value) of that cell to the sum of the cost to travel to that cell and the steering cost. The key to cooperative exploration in the team of vehicles lies in each vehicle's ability to communicate the updates of the world to the whole team and to contribute to the global list of potential destinations. And each vehicle has the capability of solving the task assignment problem for the team by calling its own online-task-assignment solving engine. This algorithm results each vehicle in having a destination to visit, which benefits the whole team the most and reduces the total exploration time of the team.
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Conte, Gianpaolo. "Vision-Based Localization and Guidance for Unmanned Aerial Vehicles." Doctoral thesis, Linköping : Department of of Computer and Information Science, Linköpings universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17767.
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Grzywna, Jason Wesley. "A flight testbed with virtual environment capabilities for developing autonomous Micro Air Vehicles." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0008441.
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Kang, Keeryun. "Online optimal obstacle avoidance for rotary-wing autonomous unmanned aerial vehicles." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44820.
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This thesis presents an integrated framework for online obstacle avoidance of rotary-wing unmanned aerial vehicles (UAVs), which can provide UAVs an obstacle field navigation capability in a partially or completely unknown obstacle-rich environment. The framework is composed of a LIDAR interface, a local obstacle grid generation, a receding horizon (RH) trajectory optimizer, a global shortest path search algorithm, and a climb rate limit detection logic.
The key feature of the framework is the use of an optimization-based trajectory generation in which the obstacle avoidance problem is formulated as a nonlinear trajectory optimization problem with state and input constraints over the finite range of the sensor. This local trajectory optimization is combined with a global path search algorithm which provides a useful initial guess to the nonlinear optimization solver. Optimization is the natural process of finding the best trajectory that is dynamically feasible, safe within the vehicle's flight envelope, and collision-free at the same time. The optimal trajectory is continuously updated in real time by the numerical optimization solver, Nonlinear Trajectory Generation (NTG), which is a direct solver based on the spline approximation of trajectory for dynamically flat systems. In fact, the overall approach of this thesis to finding the optimal trajectory is similar to the model predictive control (MPC) or the receding horizon control (RHC), except that this thesis followed a two-layer design; thus, the optimal solution works as a guidance command to be followed by the controller of the vehicle.
The framework is implemented in a real-time simulation environment, the Georgia Tech UAV Simulation Tool (GUST), and integrated in the onboard software of the rotary-wing UAV test-bed at Georgia Tech. Initially, the 2D vertical avoidance capability of real obstacles was tested in flight. Then the flight test evaluations were extended to the benchmark tests for 3D avoidance capability over the virtual obstacles, and finally it was demonstrated on real obstacles located at the McKenna MOUT site in Fort Benning, Georgia. Simulations and flight test evaluations demonstrate the feasibility of the developed framework for UAV applications involving low-altitude flight in an urban area.
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Rochefort, Yohan. "Méthodes pour le guidage coopératif." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00934428.
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L'objectif de cette thèse est de définir puis d'étudier les performances de méthodes de guidage coopératif de véhicules aériens autonomes. L'intérêt du guidage coopératif est de confier une mission complexe à une flotte, plutôt qu'à un véhicule unique, afin de distribuer la charge de travail et d'améliorer les performances et la fiabilité. Les lois de guidage étudiées sont distribuées sur l'ensemble des véhicules afin d'une part, de répartir la charge de calcul et d'autre part, d'augmenter la fiabilité en éliminant la possibilité de perte de l'organe central de calcul de la commande.La première partie de la thèse porte sur les possibilités offertes par la règle des plus proches voisins. La loi de guidage développée consiste à ce que la commande de chaque véhicule soit élaborée en combinant les états des véhicules voisins. Afin de transmettre des consignes au groupe de véhicules, des objets dénommés agents virtuels sont introduits. Ceux-ci permettent de représenter des obstacles, d'indiquer une direction ou une cible au groupe de véhicules en utilisant des mécanismes déjà présent dans la loi de guidage.La seconde partie de la thèse porte sur les possibilités offertes par la commande prédictive. Ce type de commande consiste à employer un modèle du comportement du système afin de prédire les effets de la commande, et ainsi de déterminer celle qui minimise un critère de coût en respectant les contraintes du système. La loi de guidage développée emploi un critère de coût tenant compte et arbitrant entre les différents aspects de la mission (sécurité, progression de la mission, modération de la commande), et une procédure de recherche de la commande utilisant jeu prédéfinis de commandes candidates afin d'explorer l'espace de commande de manière efficace. Cette procédure, distincte des algorithmes d'optimisation habituels, génère une charge de calcul faible et constante, ne nécessite pas d'étape d'initialisation et est très peu sensible aux minima locaux.
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Basso, Maik. "A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/179536.
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A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.<br>Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware.
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Sabiron, Guillaume. "Synthèse d’une solution GNC basée sur des capteurs de flux optique bio-inspirés adaptés à la mesure des basses vitesses pour un atterrissage lunaire autonome en douceur." Thesis, Toulouse, ISAE, 2014. http://www.theses.fr/2014ESAE0038/document.
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Dans cette thèse, nous nous intéressons au problème de l’atterrissage lunaire autonome et nous proposons une méthode innovante amenant une alternative à l’utilisation de capteurs classiques qui peuvent se révéler encombrants, énergivores et très onéreux.La première partie est consacrée au développement et à la construction de capteurs de mouvement inspirés de la vision des insectes volants et mesurant le flux optique.Le flux optique correspond à la vitesse angulaire relative de l’environnement mesurée par la rétine d’un agent. Dans un environnement fixe, les mouvements d’un robot génèrent un flux optique contenant des informations essentielles sur le mouvement de ce dernier. En utilisant le principe du « temps de passage », nous présentons les résultats expérimentaux obtenus en extérieur avec deux versions de ces capteurs.Premièrement, un capteur mesurant le flux optique dans les deux directions opposées est développé et testé en laboratoire. Deuxièmement un capteur adapté à la mesure des faibles flux optiques similaires à ceux pouvant être mesurés lors d’un alunissage est développé, caractérisé et enfin testé sur un drone hélicoptère en conditions extérieures.Dans la seconde partie, une méthode permettant de réaliser le guidage, la navigation et la commande (GNC pour Guidance Navigation and Control) du système est proposée. L’innovation réside dans le fait que l’atterrissage en douceur est uniquement assuré par les capteurs de flux optique. L’utilisation des capteurs inertiels est réduite au maximum. Plusieurs capteurs orientés dans différentes directions de visée, et fixés à la structure de l’atterrisseur permettent d’atteindre les conditions finales définies par les partenaires industriels. Les nombreuses informations décrivant la position et l’attitude du système contenues dans le flux optique sont exploitées grâce aux algorithmes de navigation qui permettent d’estimer les flux optiques ventraux et d’expansion ainsi que le tangage.Nous avons également montré qu’il est possible de contrôler l’atterrisseur planétaire en faisant suivre aux flux optiques estimés une consigne optimale au sens de la consommation d’énergie. Les simulations réalisées durant la thèse ont permis de valider le fonctionnement et le potentiel de la solution GNC proposée en intégrant le code du capteur ainsi que des images simulées du sol de la lune<br>In this PhD thesis, the challenge of autonomous lunar landing was addressed and an innovative method was developed, which provides an alternative to the classical sensor suites based on RADAR, LIDAR and cameras, which tend to be bulky, energy consuming and expensive. The first part is devoted to the development of a sensor inspired by the fly’s visual sensitivity to optic flow (OF). The OF is an index giving the relative angular velocity of the environment sensed by the retina of a moving insect or robot. In a fixed environment (where there is no external motion), the self-motion of an airborne vehicle generates an OF containing information about its own velocity and attitude and the distance to obstacles. Based on the “Time of Travel” principle we present the results obtained for two versions of 5 LMSs based optic flow sensors. The first one is able to measure accurately the OF in two opposite directions. It was tested in the laboratory and gave satisfying results. The second optic flow sensor operates at low velocities such as those liable to occur during lunar landing was developed. After developing these sensors, their performances were characterized both indoors and outdoors, and lastly, they were tested onboard an 80-kg helicopter flying in an outdoor environment. The Guidance Navigation and Control (GNC) system was designed in the second part on the basis of several algorithms, using various tools such as optimal control, nonlinear control design and observation theory. This is a particularly innovative approach, since it makes it possible to perform soft landing on the basis of OF measurements and as less as possible on inertial sensors. The final constraints imposed by our industrial partners were met by mounting several non-gimbaled sensors oriented in different gaze directions on the lander’s structure. Information about the lander’s self-motion present in the OF measurements is extracted by navigation algorithms, which yield estimates of the ventral OF, expansion OF and pitch angle. It was also established that it is possible to bring the planetary lander gently to the ground by tracking a pre-computed optimal reference trajectory in terms of the lowest possible fuel consumption. Software-in-the-loop simulations were carried out in order to assess the potential of the proposed GNC approach by testing its performances. In these simulations, the sensor firmware was taken into account and virtual images of the lunar surface were used in order to improve the realism of the simulated landings
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Holt, Ryan S. "Three Enabling Technologies for Vision-Based, Forest-Fire Perimeter Surveillance Using Multiple Unmanned Aerial Systems." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/931.
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The ability to gather and process information regarding the condition of forest fires is essential to cost-effective, safe, and efficient fire fighting. Advances in sensory and autopilot technology have made miniature unmanned aerial systems (UASs) an important tool in the acquisition of information. This thesis addresses some of the challenges faced when employing UASs for forest-fire perimeter surveillance; namely, perimeter tracking, cooperative perimeter surveillance, and path planning. Solutions to the first two issues are presented and a method for understanding path planning within the context of a forest-fire environment is demonstrated. Both simulation and hardware results are provided for each solution.
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Eikenberry, Blake D. "Guidance and navigation software architecture design for the Autonomous Multi-Agent Physically Interacting Spacecraft (AMPHIS) test bed." Thesis, Monterey California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2349.
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The Autonomous Multi-Agent Physically Interacting Spacecraft (AMPHIS) test bed examines the problem of multiple spacecraft interacting at close proximity. This thesis contributes to this on-going research by addressing the development of the software architecture for the AMPHIS spacecraft simulator robots and the implementation of a Light Detection and Ranging (LIDAR) unit to be used for state estimation and navigation of the prototype robot. The software modules developed include: user input for simple user tasking; user output for data analysis and animation; external data links for sensors and actuators; and guidance, navigation and control (GNC). The software was developed in the SIMULINK/MATLAB environment as a consistent library to serve as stand alone simulator, actual hardware control on the robot prototype, and any combination of the two. In particular, the software enables hardware-in-the-loop testing to be conducted for any portion of the system with reliable simulation of all other portions of the system. The modularity of this solution facilitates fast proof-of-concept validation for the GNC algorithms. Two sample guidance and control algorithms were developed and are demonstrated here: a Direct Calculus of Variation method, and an artificial potential function guidance method. State estimation methods are discussed, including state estimation from hardware sensors, pose estimation strategies from various vision sensors, and the implementation of a LIDAR unit for state estimation. Finally, the relative motion of the AMPHIS test bed is compared to the relative motion on orbit, including how to simulate the on-orbit behavior using Hill's equations.
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Bakolas, Efstathios. "Optimal steering for kinematic vehicles with applications to spatially distributed agents." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42873.
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The recent technological advances in the field of autonomous vehicles have resulted in a growing impetus for researchers to improve the current framework of mission planning and execution within both the military and civilian contexts. Many recent efforts towards this direction emphasize the importance of replacing the so-called monolithic paradigm, where a mission is planned, monitored, and controlled by a unique global decision maker, with a network centric paradigm, where the same mission related tasks are performed by networks of interacting decision makers (autonomous vehicles). The interest in applications involving teams of autonomous vehicles is expected to significantly grow in the near future as new paradigms for their use are constantly being proposed for a diverse spectrum of real world applications.
One promising approach to extend available techniques for addressing problems involving a single autonomous vehicle to those involving teams of autonomous vehicles is to use the concept of Voronoi diagram as a means for reducing the complexity of the multi-vehicle problem. In particular, the Voronoi diagram provides a spatial partition of the environment the team of vehicles operate in, where each element of this partition is associated with a unique vehicle from the team. The partition induces, in turn, a graph abstraction of the operating space that is in a one-to-one correspondence with the network abstraction of the team of autonomous vehicles; a fact that can provide both conceptual and analytical advantages during mission planning and execution. In this dissertation, we propose the use of a new class of Voronoi-like partitioning schemes with respect to state-dependent proximity (pseudo-) metrics rather than the Euclidean distance or other generalized distance functions, which are typically used in the literature. An important nuance here is that, in contrast to the Euclidean distance, state-dependent metrics can succinctly capture system theoretic features of each vehicle from the team (e.g., vehicle kinematics), as well as the environment-vehicle interactions, which are induced, for example, by local winds/currents. We subsequently illustrate how the proposed concept of state-dependent Voronoi-like partition can induce local control schemes for problems involving networks of spatially distributed autonomous vehicles by examining different application scenarios.
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Lu, Wun-You, and 盧玟攸. "Intelligent Autonomous Vehicle with LiDAR Guidance." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/22593470212047587117.
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碩士<br>國立雲林科技大學<br>機械工程系<br>103<br>The thesis presents a method for the automatic interior cruise and obstacle avoidance of
Intelligent Autonomous Vehicle (IAV) with the guidance of Light Detection and
Ranging sensor (LiDAR). The main point of the thesis is to enhance the cruise speed
and obstacle avoidance functions for IAV "Yun-Trip I" and develop its advanced version
IAV "Yun-Trip II". The advanced "Yun-Trip II" features chassis from its predecessor,
improve computer vision with a single LiDAR as core, adopted ARM-Based embedded
system GNU/Linux, and perform cruising calculation and route locating with Microsoft
Visual C++. The thesis adopt VFH+ obstacle avoidance algorithm, computing
information collected by LiDAR and providing multiple locating methods to help
complete interior cruise.
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CARPENTIERO, MARCO. "Autonomous vehicle guidance in unknown environments." Doctoral thesis, 2020. http://hdl.handle.net/11573/1360316.
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Gaining from significant advances in their performance granted by technological evolution, Autonomous Vehicles are rapidly increasing the number of fields of possible and effective applications. From operations in hostile, dangerous environments (military use in removing unexploded projectiles, survey of nuclear power and chemical industrial plants following accidents) to repetitive 24h tasks (border surveillance), from power-multipliers helping in production to less exotic commercial application in household activities (cleaning robots as consumer electronics products), the combination of autonomy and motion offers nowadays impressive options. In fact, an autonomous vehicle can be completed by a number of sensors, actuators, devices making it able to exploit a quite large number of tasks. However, in order to successfully attain these results, the vehicle should be capable to navigate its path in different, sometimes unknown environments. This is the goal of this dissertation: to analyze and - mainly - to propose a suitable solution for the guidance of autonomous vehicles. The frame in which this research takes its steps is the activity carried on at the Guidance and Navigation Lab of Sapienza – Università di Roma, hosted at the School of Aerospace Engineering. Indeed, the solution proposed has an intrinsic, while not limiting, bias towards possible space applications, that will become obvious in some of the following content. A second bias dictated by the Guidance and Navigation Lab activities is
represented by the choice of a sample platform. In fact, it would be difficult to perform a meaningful study keeping it a very general level, independent on the characteristics of the targeted kind of vehicle: it is easy to see from the rough list of applications cited above that these characteristics are extremely varied. The Lab hosted – even before the beginning of this thesis activity – a simple, home-designed and manufactured model of a small, yet performing enough autonomous vehicle, called RAGNO (standing for Rover for Autonomous Guidance Navigation and Observation): it was an obvious choice to select that rover as the reference platform to identify solutions for guidance, and to use it, cooperating to its improvement, for the test activities which should be considered as mandatory in this kind of thesis work to validate the suggested approaches.
The draft of the thesis includes four main chapters, plus introduction, final remarks and future perspectives, and the list of references.
The first chapter (“Autonomous Guidance Exploiting Stereoscopic Vision”) investigates in detail the technique which has been deemed as the most interesting for small vehicles. The current availability of low cost, high performance cameras suggests the adoption of the stereoscopic vision as a quite effective technique, also capable to making available to remote crew a view of the scenario quite similar to the one humans would have. Several advanced image analysis techniques have been investigated for the extraction of the features from left- and right-eye images, with SURF and BRISK algorithm being selected as the most promising one. In short, SURF is a blob detector with an associated descriptor of 64 elements, where the generic feature is extracted by applying sequential box filters to the surrounding area. The features are then localized in the point of the image where the determinant of the Hessian matrix H(x,y) is maximum. The descriptor vector is than determined by calculating the Haar wavelet response in a sampling pattern centered in the feature. BRISK is instead a corner detector with an associated binary descriptor of 512 bit. The generic feature is identified as the brightest point in a sampling circular area of N pixels while the descriptor vector is calculated by computing the brightness gradient of each of the N(N-1)/2 pairs of sampling points. Once left and right features have been extracted, their descriptors are compared in order to determine the corresponding pairs. The matching criterion consists in seeking for the two descriptors for which their relative distance (Euclidean norm for SURF, Hamming distance for BRISK) is minimum. The matching process is computationally expensive: to reduce the required time the thesis successfully explored the theory of the
epipolar geometry, based on the geometric constraint existing between the left and right projection of the scene point P, and indeed limiting the space to be searched. Overall, the selected techniques require between 200 and 300 ms on a 2.4GHz clock CPU for the feature extraction and matching in a single (left+right) capture, making it a feasible solution for slow motion vehicles. Once matching phase has been finalized, a disparity map can be prepared highlighting the position of the identified objects, and by means of a triangulation (the baseline between the two cameras is known, the size of the targeted object is measured in pixels in both images) the position and distance of the obstacles can be obtained.
The second chapter (“A Vehicle Prototype and its Guidance System”) is devoted to the implementation of the stereoscopic vision onboard a small test vehicle, which is the previously cited RAGNO rover. Indeed, a description of the vehicle – the chassis, the propulsion system with four electric motors empowering the wheels, the good roadside performance attainable, the commanding options – either fully autonomous, partly autonomous with remote monitoring, or fully remotely controlled via TCP/IP on mobile networks - is included first, with a focus on different sensors that, depending on the scenario, can integrate the stereoscopic vision system. The intelligence-side of guidance subsystem, exploiting the navigation information provided by the camera, is then detailed. Two guidance techniques have been studied and implemented to identify the optimal trajectory in a field with scattered obstacles: the artificial potential guidance, based on the Lyapunov approach, and the A-star algorithm, looking for the minimum of a cost function built on graphs joining the cells of a mesh over-imposed to the scenario. Performance of the two techniques are assessed for two specific test-cases, and the possibility of unstable behavior of the artificial potential guidance, bouncing among local minima, has been highlighted. Overall, A-star guidance is the suggested solution in terms of time, cost and reliability. Notice that, withstanding the noise affecting information from sensors, an estimation process based on Kalman filtering has been also included in the process to improve the smoothness of the targeted trajectory.
The third chapter (“Examples of Possible Missions and Applications”) reports two experimental campaigns adopting RAGNO for the detection of dangerous gases. In the first one, the rover accommodates a specific sensor, and autonomously moves in open fields, avoiding possible obstacles, to exploit measurements at given time intervals. The same
configuration for RAGNO is also used in the second campaign: this time, however, the path of the rover is autonomously computed on the basis of the way points communicated by a drone which is flying above the area of measurements and identifies possible targets of interest.
The fourth chapter (“Guidance of Fleet of Autonomous Vehicles ”) stresses this successful idea of fleet of vehicles, and numerically investigates by algorithms purposely written in Matlab the performance of a simple swarm of two rovers exploring an unknown scenario, pretending – as an example - to represent a case of planetary surface exploration. The awareness of the surrounding environment is dictated by the characteristics of the sensors accommodated onboard, which have been assumed on the basis of the experience gained with the material of previous chapter. Moreover, the communication issues that would likely affect real world cases are included in the scheme by the possibility to model the comm link, and by running the simulation in a multi-task configuration where the two rovers are assigned to two different computer processes, each of them having a different TCP/IP address with a behavior actually depending on the flow of information received form the other explorer. Even if at a simulation-level only, it is deemed that such a final step collects different aspects investigated during the PhD period, with feasible sensors’ characteristics (obviously focusing on stereoscopic vision), guidance technique, coordination among autonomous agents and possible interesting application cases.