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Peterson, John Ryan. "Autonomous Source Localization." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97954.
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This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments.<br>Doctor of Philosophy<br>This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.
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Cavallin, Kristoffer, and Peter Svensson. "Semi-Autonomous,Teleoperated Search and Rescue Robot." Thesis, Umeå University, Department of Computing Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-31928.
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<p>The interest in robots in the urban search and rescue (USAR) field has increased the last two decades. The idea is to let robots move into places where human rescue workers cannot or, due to high personal risks, should not enter.In this thesis project, an application is constructed with the purpose of teleoperating a simple robot. This application contains a user interface that utilizes both autonomous and semi-autonomous functions, such as search, explore and point-and-go behaviours. The purpose of the application is to work with USAR principles in a refined and simplified environment, and thereby increase the understanding for these principles and how they interact with each other. Furthermore, the thesis project reviews the recent and the current status of robots in USAR applications and use of teleoperation and semi-autonomous robots in general. Some conclusions that are drawn towards the end of the thesis are that the use of robots, especially in USAR situations, will continue to increase. As robots and support technology both become more advanced and cheaper by the day, teleoperation and semi-autonomous robots will also be seen in more and more places.</p><p> </p>
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Earnest, Caleb A. "Dynamic action spaces for autonomous search operations." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/46549.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.<br>Includes bibliographical references (p. 148-150).<br>This thesis presents a new approach for a Navy unmanned undersea vehicle (UUV) to search for and detect an evading contact. This approach uses a contact position distribution from a generic particle filter to estimate the state of a single moving contact and to plan the path that minimizes the uncertainty in the location of the contact. The search algorithms introduced in this thesis will implement a motion planner that searches for a contact with the following information available to the decision system: (1) null measurement (i.e., contact not detected at current time), (2) timedated measurement (i.e., clue found at current time that indicates contact was at this location in the past), and (3) bearings measurement (i.e., angular measurement towards contact position detected at current time). The results of this thesis will be arrived at by evaluating the best methods to utilize the three types of information. The underlying distribution of the contact state space will be modeled using a generic particle filter, due to the highly non-Gaussian distributions that result from the conditions mentioned above. Using the particle filter distribution and the measurements acquired from the three conditions, this thesis will work towards implementing a path planning algorithm that creates dynamic action spaces that evaluate the uncertainty of position distribution. Ultimately, the path planner will choose the path that contains the position distribution and leads to sustained searches.<br>by Caleb A. Earnest.<br>S.M.
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Beck, Zoltan. "Collaborative search and rescue by autonomous robots." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/411031/.
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In recent years, professional first responders have started to use novel technologies at the scene of disasters in order to save more lives. Increasingly, they use robots to search disaster sites. One of the most widely and successfully used robot platforms in the disaster response domain are unmanned aerial vehicles (UAVs). UAVs allow remote inspection and mapping. They are able to provide high resolution imagery and often need minimal infrastructure to fly. This allows settings where multiple UAVs are airborne accelerating the information gathering from the disaster site. However, current deployments use labour intensive, individually teleoperated UAVs. Given this, there is a drive toward using multiple robots operating with a certain level of autonomy, in order to decrease the operators' workload. One approach for utilising multiple robots in this way is semi-autonomous operation supervised by a small number of professionals; only requiring human experts for crucial decisions. Current commercial UAV platforms also allow the deployment of a diverse group of robots, allowing them to combine their individual capabilities to be more ecient. For example, xed-wing UAVs are capable of flying faster and carry larger payload, but when they do so, they should be deployed with higher safety measures (safety pilots are required for non-lightweight aircraft). On the other hand, small rotary-wing UAVs are more agile and can approach and provide imagery about objects on the ground. To this end, this thesis develops a number of new approaches for the collaboration of a heterogeneous group of robots in disaster response. More specifically, the problem of collaborative planning with robots operating in an uncertain workflow based setting is investigated by solving the search and rescue (SAR) collaboration problem. Of course, the problem complexity increases when collaborating with dierent robots. It is not different in this setting, the actions of dierent types of robots need to be planned with dependencies between their actions under uncertainty. To date, research on collaboration between multiple robots has typically focused on known settings, where the possible robot actions are dened as a set of tasks. However, in most real world settings, there is a signicant amount of uncertainty present. For ii example, information about a disaster site develops gradually during disaster relief, thus initially there is often very little certainty about the locations of people requiring assistance (e.g. damaged buildings, trapped victims, or supply shortages). Existing solutions that tackle collaboration in the face of uncertain information are typically limited to simple exploration or target search problems. Moreover, the use of generic temporal planners rapidly becomes intractable for such problems unless applied in a domain-specific manner. Finally, domain specific approaches rarely involve complex action relations, such as task dependencies where the actions of some robots are built on the actions of others. When they do so, decomposition techniques are applied to decrease the problem complexity, or simple heuristics are applied to enhance similar collaboration. Such approaches often lead to low quality solutions, because vital action dependencies across different roles are not taken into account during the optimisation. Against this background, we oer novel online planning approaches for heterogeneous multi-robot collaboration under uncertainty. First, we provide a negotiation-based bidirectional collaborative planning approach that exploits the potential in determinisation via hindsight optimisation (HOP) combined with long-term planning. Second, we extend this approach to create an anytime Monte Carlo tree search planner that also utilises HOP combined with long-term planning. In online planning settings, such as SAR, anytime planners are benecial to ensure the ability of providing a feasible plan within the given computational budget. Third, we construct a scenario close to physical deployment that allows us to show how our long-term collaborative planning outperforms the current state of the art path-planning approaches by 25 %. We conclude that long-term collaborative planning under uncertainty provides an improvement when planning in SAR settings. When combined, the contributions presented in this thesis represent an advancement in the state of the art in the eld of online planning under uncertainty. The approaches and methods presented can be applied in collaborative settings when uncertainty plays an important role for defining dependencies between partial planning problems.
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Hammerseth, Vegard B. "Autonomous Unmanned Aerial Vehicle In Search And Rescue." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22880.
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This report presents a way of using autonomous drones to enhance search and rescue operations and takes the first steps in bringing the system to life. By using autonomous drones, less experience is required by the rescue personnel and drone specialists become excessive in this matter. Due to autonomy a drone can operate outside a valid radio link. Hence, when signal is lost, the craft can continue to search, buffer the information and send it when the link becomes active. By creating affordable drones the threshold decreases for deploying a unit in bad weather or other missions where the feedback is more important than drone return.Because the drones must sweep a large area, an aeroplane is the best suitable airframe. To generate less drag and increase stability; long, slender and thin-as-possible wings are recommended. To achieve aerodynamic stability, non-swept wings and a small positive dihedral angle is also advised. The wings should be attached on top of a slender and small-as-possible fuselage. However, due to the difficulties in landing and vulnerabilities related to this, a flying wing which obey the same design requirements, may be a better choice. A prototype for a flying wing made in expanded polypropylene was put together and tested. It proved to be resilient, able to withstand significant abuse, quickly recover to its former structure and be repaired in minutes. Highly convenient for various landing areas.An attitude and heading reference system (AHRS) is required to tell which orientation a vehicle may have. An affordable version has been realised by using micro electro mechanical sensors and a micro controller. Since the vehicle must orient itself within a search area, a NAVSTAR Global Positioning System (GPS) and way-point approach were drones are pre-programmed to follow a path has been developed together with a complete flight system.The drones search path must be programmed automatically from the given search area by the rescue personnel. This requires software and active communication link between a ground station and drones. An intuitive graphical user interface has been developed and verified to work by marking an area in the program and send coordinates over a commercial communication link to a prototype drone.A thermal imaging camera can be used to detect humans. It will make mammals stand out in an environment when viewed in the produced image (thermogram). Together with an arbitrarily threshold limit, the operators can be notified with coordinates when the threshold is reached. An experiment was carried out by placing a human at known coordinates. A drone with self position equipment was sent over the human repeatedly, automatically locating the person and reporting its location. It was concluded that the drone would be within 20 meters radius of the person.The generality and affordability of the airframe, AHRS and vision system can be useful for the university in the years ahead and the threshold for realising applications which uses any of these systems has therefore been reduced. A drone based system like this can enhance search and rescue and assist the majority by saving time, money and lives.
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Ryu, Kun Jin. "Autonomous Robotic Strategies for Urban Search and Rescue." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19194.
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This dissertation proposes autonomous robotic strategies for urban search and rescue (USAR) which are map-based semi-autonomous robot navigation and fully-autonomous robotic search, tracking, localization and mapping (STLAM) using a team of robots. Since the prerequisite for these solutions is accurate robot localization in the environment, this dissertation first presents a novel grid-based scan-to-map matching technique for accurate simultaneous localization and mapping (SLAM). At every acquisition of a new scan and estimation of the robot pose, the proposed technique corrects the estimation error by matching the new scan to the globally defined grid map. To improve the accuracy of the correction, each grid cell of the map is represented by multiple normal distributions (NDs). The new scan to be matched to the map is also represented by NDs, which achieves the scan-to-map matching by the ND-to-ND matching. In the map-based semi-autonomous robot navigation strategy, a robot placed in an environment creates the map of the environment and sends it to the human operator at a distant location. The human operator then makes decisions based on the map and controls the robot via tele-operation. In case of communication loss, the robot semi-autonomously returns to the home position by inversely tracking its trajectory with additional optimal path planning. In the fully-autonomous robotic solution to USAR, multiple robots communicate one another while operating together as a team. The base station collects information from each robot and assigns tasks to the robots. Unlike the semi-autonomous strategy there is no control from the human operator. To further enhance the<br />efficiency of their cooperation each member of the team specifically works on its own task.<br />A series of numerical and experimental studies were conducted to demonstrate the applicability of the proposed solutions to USAR scenarios. The effectiveness of the scan-to-map matching with the multi-ND representation was confirmed by analyzing the error accumulation and by comparing with the single-ND representation. The applicability of the scan-to-map matching to the real SLAM problem was also verified in three different real environments. The results of the map-based semi-autonomous robot navigation showed the effectiveness of the approach as an immediately usable solution to USAR. The effectiveness of the proposed fully- autonomous solution was first confirmed by two real robots in a real environment. The cooperative performance of the strategy was further investigated using the developed platform- and hardware-in-the-loop simulator. The results showed significant potential as the future solution to USAR.<br>Ph. D.
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Finegan, Edward Graham. "Intelligent Autonomous Data Categorization." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd/1343.
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The goal of this research was to determine if the results of a simple comparison algorithm (SCA) could be improved by adding a hyperspace analogue to language model of memory (HAL) layer to form NCA. The HAL layer provides contextual data that otherwise would be unavailable for consideration. It was found that NCA did improve the results when compared to SCA alone. However, NCA added complexity problems that limit its practicality. The complexity of this algorithm is On3 where n is equal to the number of unique symbols in the data. While there is a relativity reasonable soft upper bound for the number of unique symbols used in a language, the complexity still limits the uses of the NCA combined algorithm. The conclusion from this research is that NCA can improve results. This research also suggested that the quality of results might increase as more data is processed by NCA.
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Compton, Mark A. "Minefield search and object recognition for autonomous underwater vehicles." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/30604.
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Ambrose-Thurman, Andrew Michael Luke. "Autonomous, collaborative, unmanned aerial vehicles for search and rescue." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/10652/.
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Search and Rescue is a vitally important subject, and one which can be improved through the use of modern technology. This work presents a number of advances aimed towards the creation of a swarm of autonomous, collaborative, unmanned aerial vehicles for land-based search and rescue. The main advances are the development of a diffusion based search strategy for route planning, research into GPS (including the Durham Tracker Project and statistical research into altitude errors), and the creation of a relative positioning system (including discussion of the errors caused by fast-moving units). Overviews are also given of the current state of research into both UAVs and Search and Rescue.
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Beyme, Steffen. "Autonomous, wireless sensor network-assisted target search and mapping." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50725.
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The requirements of wireless sensor networks for localization applications are largely dictated by the need to estimate node positions and to establish routes to dedicated gateways for user communication and control. These requirements add significantly to the cost and complexity of such networks.
In some applications, such as autonomous exploration or search and rescue, which may benefit greatly from the capabilities of wireless sensor networks, it is necessary to guide an autonomous sensor and actuator platform to a target, for example to acquire a large data payload from a sensor node, or to retrieve the target outright.
We consider the scenario of a mobile platform capable of directly interrogating individual, nearby sensor nodes. Assuming that a broadcast message originates from a source node and propagates through the network by flooding, we study applications of autonomous target search and mapping, using observations of the message hop count alone. Complex computational and communication tasks are offloaded from the sensor nodes, leading to significant simplifications of the node hardware and software.
This introduces the need to model the hop count observations made by the mobile platform to infer node locations. Using results from first-passage percolation theory and a maximum entropy argument, we formulate a stochastic jump process which approximates the message hop count at distance r from the source. We show that the marginal distribution of this process has a simple analytic form whose parameters can be learned by maximum likelihood estimation.
Target search involving an autonomous mobile platform is modeled as a stochastic planning problem, solved approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by an open-loop search plan in which path constraints and assumptions about future information gains are relaxed. It is shown that the performance is improved over typical information-driven approaches.
Finally, the hop count observation model is applied to an autonomous mapping problem. The platform is guided under a myopic utility function which quantifies the expected information gain of the inferred map. Utility function parameters are adapted heuristically such that map inference improves, without the cost penalty of true non-myopic planning.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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Burian, Erik Alfred. "Search methods for an autonomous underwater vehicle using scalar measurements." Thesis, Springfield, Va. : Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA314610.
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Shafer, Andrew J. M. Eng Massachusetts Institute of Technology. "Autonomous cooperation of heterogeneous platforms for sea-based search tasks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45650.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 87-90).<br>Many current methods of search using autonomous marine vehicles do not adapt to changes in mission objectives or the environment. A cellular-decomposition-based framework for cooperative, adaptive search is proposed that allows multiple search platforms to adapt to changes in both mission objectives and environmental parameters. Software modules for the autonomy framework MOOS-IvP are described that implement this framework. Simulated and experimental results show that it is feasible to combine both pre-planned and adaptive behaviors to eectively search a target area.<br>by Andrew J. Shafer.<br>M.Eng.
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Helland, Peter D. "PURL II, a rapid deployment search and survey autonomous underwater vehicle." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq24152.pdf.
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Giamou, Matthew Peter. "Place recognition for GNSS-denied autonomous multi-robot search and rescue." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112422.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-85).<br>Teams of agile unmanned aerial vehicles (UAVs) possess great potential for search and rescue missions requiring a rapid response over a large region of interest. With proper coordination, these robotic vehicles can leverage affordable hardware to efficiently search a remote region or disaster site for lost or injured people. However, effective search coordination requires that the robots successfully fuse information from their environment into an accurate and consistent localization and mapping scheme in order to ensure the entire region of interest is explored. This requires that the robots communicate observations of their environment or other robots to produce inter-robot trajectory and map constraints. The difficulty of this task is exacerbated in areas without Global Navigation Satellite System (GNSS) coverage, as absolute pose measurements are unavailable. This thesis explores solutions to the place recognition problem for UAVs under a dense forest canopy. The perception and communication challenges in a forest environment are explored for a multi-UAV system. A survey of existing place-recognition and multi-agent simultaneous localization and mapping (SLAM) systems is conducted and several candidate approaches are discussed, and a multi-agent pose-SLAM formulation is introduced as a practical framework. A state-of-the-art laser-based place recognition system is implemented and augmented with a Dirichlet process means (DP-means) clustering for stable feature selection. Finally, recent results describing some graph theoretic properties of SLAM problems are used in a resource-constrained SLAM framework. Experimental data collected from Middlesex Fells Reservation is used to validate the algorithms presented.<br>by Matthew Peter Giamou.<br>S.M.
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Gabrecht, Katharina M. "Human factors of semi-autonomous robots for urban search and rescue." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/35458/.
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During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place. The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress. Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels. Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks. In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels. Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research. However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date. This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots.
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Savage, Elizabeth. "Cooperative control of autonomous underwater vehicles." [College Station, Tex. : Texas A&M University, 2003. http://hdl.handle.net/1969.1/236.
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Thesis (MS)--Texas A&M University, 2003.<br>"Major Subject: Aerospace Engineering" Title from author supplied metadata (automated record created on Jul. 18, 2005.) Vita. Abstract. Includes bibliographical references.
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Dockhorn, Alexander Verfasser], and Rudolf [Gutachter] [Kruse. "Prediction-based search for autonomous game-playing / Alexander Dockhorn ; Gutachter: Rudolf Kruse." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2020. http://d-nb.info/1220035726/34.
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Rodrigues, Neto Jose Augusto. "A mine search algorithm for the Naval Postgraduate School Autonomous Underwater Vehicle." Thesis, Monterey, California. Naval Postgraduate School, 1994. http://hdl.handle.net/10945/30542.
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Approved for public release; distribution unlimited.<br>This thesis develops, implements and tests a mine search algorithm for the Naval Postgraduate School Autonomous Underwater Vehicle (Phoenix). The vehicle is 72 inches long and displaces 400 pounds. Its maneuvers are performed using two propellers and four thrustem. It contains two embedded computer systems. The algorithm directs the autonomous search of a specified area mapping all obstacles and computing an estimate of the cumulative probability of detection. The algorithm uses no prior knowledge of the terrain or the location of mines. The algorithm, which is written in Lisp, can execute on the vehicle's computer systems. Along with the search and mapping capabilities, the algorithm executes obstacle avoidance. The algorithm is tested in several simulated scenarios with different placement of mines and obstacles; the amount of resources used and the fraction of the area searched is computed. A similar algorithm that uses hill-climbing search is implemented for comparison. In all cases, the newly developed algorithm performed equal or better than the one that uses hill-climbing.
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Dockhorn, Alexander [Verfasser], and Rudolf [Gutachter] Kruse. "Prediction-based search for autonomous game-playing / Alexander Dockhorn ; Gutachter: Rudolf Kruse." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2020. http://d-nb.info/1220035726/34.
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FLINT, MATTHEW D. "COOPERATIVE CONTROL FOR MULTIPLE AUTONOMOUS UAV's SEARCHING FOR TARGETS IN AN UNCERTAIN ENVIRONMENT." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1021923726.
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Hasler, Michael Douglas. "Simulation for Improvement of Dynamic Path Planning in Autonomous Search and Rescue Robots." Thesis, University of Canterbury. Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/4475.
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To hasten the process of saving lives after disasters in urban areas, autonomous robots are being looked to for providing mapping, hazard identification and casualty location. These robots need to maximise time in the field without having to recharge and without reducing productivity. This project aims to improve autonomous robot navigation through allowing comparison of algorithms with various weightings, in conjunction with the ability to vary physical parameters of the robot and other factors such as error thresholds/limits.
The lack of a priori terrain data in disaster sites, means that robots have to dynamically create a representation of the terrain from received sensor range-data in order to path plan. To reduce the resources used, the affect of input data on the terrain model is analysed such that some points may be culled. The issues of
identifying hazards within these models are considered with respect to the effect on safe navigation.
A modular open-source platform has been created which allows the automated
running of experimental trials in conjunction with the implementation and use of other input types, node networks, or algorithms. Varying the terrains, obstacles, initial positions and goals, which a virtual robot is tasked with navigating means that the design, and hence performance, are not tailored to individual situations. Additionally, this demonstrates the variability of scenarios possible. This combination of features allows one to identify the effects of different design decisions, while the use of a game-like graphical interface allows users to readily view and comprehend the scenarios the robot encounters and the paths produced to traverse these environments. The initially planned focus of experimentation lay in testing different algorithms and various weightings, however this was expanded
to include different implementations and factors of the input collection, terrain modelling and robot movement. Across a variety of terrain scenarios, the resultant paths and status upon trial completion were analysed and displayed to allow observations to be made.
It was found that the path planning algorithms are of less import than initially believed, with other facets of the robotic system having equally significant roles in producing quality paths through a hazardous environment. For fixed view robots, like the choice used in this simulator, it was found that there were issues of incompatibility with A* based algorithms, as the algorithm’s expected knowledge of the areas in all directions regardless of present orientation, and hence they did not perform as they are intended. It is suggested that the behaviour of such algorithms be modified if they are to be used with fixed view systems, in order to gather sufficient data from the surroundings to operate correctly and find paths in difficult terrains.
A simulation tool such as this, enables the process of design and testing to be completed with greater ease, and if one can restrain the number of parameters varied, then also with more haste. These benefits will make this simulation tool a valuable addition to the field of USAR research.
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Jönsson, Jonatan, and Felix Stenbäck. "Monte-Carlo Tree Search in Continuous Action Spaces for Autonomous Racing : F1-tenth." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42442.
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Autonomous cars involve problems with control and planning. In thispaper, we implement and evaluate an autonomous agent based ona Monte-Carlo Tree Search in continuous action space. To facilitatethe algorithm, we extend an existing simulation framework and usea GPU for faster calculations. We compare three action generatorsand two rewards functions. The results show that MCTS convergesto an effective driving agent in static environments. However, it onlysucceeds at driving slow speeds in real-time. We discuss the problemsthat arise in dynamic and static environments and look to future workin improving the simulation tool and the MCTS algorithm. See code, https://github.com/felrock/PyRacecarSimulator
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Ong, Seow Meng. "A Mission Planning Expert System with Three-Dimensional Path Optimization for the NPS Model 2 Autonomous Underwater Vehicle." Thesis, Monterey, California. Naval Postgraduate School, 1990. http://hdl.handle.net/10945/23457.
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Approved for public release; distribution is unlimited<br>Unmanned vehicle technology has matured significantly over the last two decades. This is evidenced by its widespread use in industrial and military applications ranging from deep-ocean exploration to anti-submarine warefare. Indeed, the feasiblity of short-range, special-purpose vehicles (whether aunonomous or remotely operated) is no longer in question. The research efforts have now begun to shift their focus on development of reliable, longer-range, high-endurance and fully autonomous systems. One of the major underlying technologies required to realize this goal is Artificial Intelligence (AI). The latter offers great potential to endow vehicles with the intelligence needed for full autonomy and extended range capability; this involves the increased application of AI technologies to support mission planning and execution, navigation and contingency planning. This thesis addresses two issues associated with the above goal for Autonomous Underwater Vehicles (AUV's). Firstly, a new approach is proposed for path planning in underwater environments that is capable of dealing with uncharted obstacles and which requires significantly less planning time and computer memory. Secondly, it explores the use of expert system technology in the planning of AUV missions.
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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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Hardin, Benjamin C. "On Autonomous Multi-agent Control in Wilderness Search and Rescue: A Mixed Initiative Approach." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2589.pdf.
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Wang, Yue. "Decision-Making for Search and Classification using Multiple Autonomous Vehicles over Large-Scale Domains." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-dissertations/87.
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This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms.
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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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Nordström, Oskar, and Alexander Axelsson. "Pathfinder : Autonomous Guided Vehicle using Infrared Light." Thesis, KTH, Maskinkonstruktion (Inst.), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230241.
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I världen växer forskning på självgående fordon dagligen.Målet med detta projekt var att skapa ett självgåendefordon och utforska möjligheterna att använda infrarödareflektioner som navigeringsmetod och hur man kanuppnå distinkta mätvärden. Avhandlingen diskuterar ävenmöjligheterna att använda flera prototyper i en störreskala. Under projektets gång byggdes ett prototypfordonför att genomföra experimenten angående lämplighetenmed navigering via infrarött ljus. Tester med prototypenvisar att navigering via infrarött ljus är väldigt pålitligtunder kontrollerade omständigheter. Projektet utforskaräven hur hierarkisk mjukvaruarkitektur står sig mot heltlokal eller centraliserad mjukvaruarkitektur.<br>In the world, research on autonomous navigation vehicles(AGV) is growing by the day. The goal with this projectwas to create an AGV and explore the possibility of usinginfrared reflections as a navigational method and how toachieve distinct reflection measurements from a surface.The thesis also discusses the possibility of using severalunits on a larger scale. In the progress of the project, aprototype vehicle was built to conduct the experiments toidentify the suitability of infrared navigation. The testingof the prototype shows that navigation by IR can be veryreliable under controlled circumstances. The project alsoexplored how hierarchical software architecture stands incomparison to purely local or centralized software architecture.
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Nouyan, Shervin. "Teamwork in a swarm of robots: an experiment in search and retrieval." Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210476.
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In this thesis, we investigate the problem of path formation and prey retrieval in a swarm of robots. We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed<p>to the chain structure. The second mechanism is called vectorfield. In this case,<p>the robots form a pattern that globally indicates the direction towards a goal or<p>home location. Both algorithms were designed following the swarm robotics control<p>principles: simplicity of control, locality of sensing and communication, homogeneity<p>and distributedness.<p><p>We test each controller on a task that consists in forming a path between two<p>objects—the prey and the nest—and to retrieve the prey to the nest. The difficulty<p>of the task is given by four constraints. First, the prey requires concurrent, physical<p>handling by multiple robots to be moved. Second, each robot’s perceptual range<p>is small when compared to the distance between the nest and the prey; moreover,<p>perception is unreliable. Third, no robot has any explicit knowledge about the<p>environment beyond its perceptual range. Fourth, communication among robots is<p>unreliable and limited to a small set of simple signals that are locally broadcast.<p><p>In simulation experiments we test our controllers under a wide range of conditions,<p>changing the distance between nest and prey, varying the number of robots<p>used, and introducing different obstacle configurations in the environment. Furthermore,<p>we tested the controllers for robustness by adding noise to the different sensors,<p>and for fault tolerance by completely removing a sensor or actuator. We validate the<p>chain controller in experiments with up to twelve physical robots. We believe that<p>these experiments are among the most sophisticated examples of self-organisation<p>in robotics to date.<br>Doctorat en Sciences de l'ingénieur<br>info:eu-repo/semantics/nonPublished
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Rafferty, Kevin John. "A comparison study of search heuristics for an autonomous multi-vehicle air-sea rescue system." Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/5292/.
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The immense power of the sea presents many life-threatening dangers to humans, and many fall foul of its unforgiving nature. Since manned rescue operations at sea (and indeed other search and rescue operations) are also inherently dangerous for rescue workers, it is common to introduce a level of autonomy to such systems. This thesis investigates via simulations the application of various search algorithms to an autonomous air-sea rescue system, which consists of an unmanned surface vessel as the main hub, and four unmanned helicopter drones. The helicopters are deployed from the deck of the surface vessel and are instructed to search certain areas for survivors of a stricken ship. The main aim of this thesis is to investigate whether common search algorithms can be applied to the autonomous air-sea rescue system to carry out an efficient search for survivors, thus improving the present-day air-sea rescue operations. Firstly, the mathematical model of the helicopter is presented. The helicopter model consists of a set of differential equations representing the translational and rotational dynamics of the whole body, the flapping dynamics of the main rotor blades, the rotor speed dynamics, and rotational transformations from the Earth-fixed frame to the body frame. Next, the navigation and control systems are presented. The navigation system consists of a line-of-sight autopilot which points each vehicle in the direction of its desired waypoint. Collision avoidance is also discussed using the concept of a collision cone. Using the mathematical models, controllers are developed for the helicopters: Proportional-Integral-Derivative (PID) and Sliding Mode controllers are designed and compared. The coordination of the helicopters is carried out using common search algorithms, and the theory, application, and analysis of these algorithms is presented. The search algorithms used are the Random Search, Hill Climbing, Simulated Annealing, Ant Colony Optimisation, Genetic Algorithms, and Particle Swarm Optimisation. Some variations of these methods are also tested, as are some hybrid algorithms. As well as this, three standard search patterns commonly used in maritime search and rescue are tested: Parallel Sweep, Sector Search, and Expanding Square. The effect of adding to the objective function a probability distribution of target locations is also tested. This probability distribution is designed to indicate the likely locations of targets and thus guide the search more effectively. It is found that the probability distribution is generally very beneficial to the search, and gives the search the direction it needs to detect more targets. Another interesting result is that the local algorithms perform significantly better when given good starting points. Overall, the best approach is to search randomly at the start and then hone in on target areas using local algorithms. The best results are obtained when combining a Random Search with a Guided Simulated Annealing algorithm.
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Nordström, Oskar, and ALEXANDER AXELSSON. "Pathfinder : Autonomous Guided Vehicle using Infrared Light." Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232997.
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In the world, research on autonomous navigation vehicles (AGV) is growing by the day. The goal with this project was to create an AGV and explore the possibility of using infrared reflections as a navigational method and how to achieve distinct reflection measurements from a surface. The thesis also discusses the possibility of using several units on a larger scale. In the progress of the project, a prototype vehicle was built to conduct the experiments to identify the suitability of infrared navigation. The testing of the prototype shows that navigation by IR can be very reliable under controlled circumstances. The project also explored how hierarchical software architecture stands in comparison to purely local or centralized software architecture.<br>I världen växer forskning på självgående fordon dagligen. Målet med detta projekt var att skapa ett självgående fordon och utforska möjligheterna att använda infraröda reflektioner som navigeringsmetod och hur man kan uppnå distinkta mätvärden. Avhandlingen diskuterar även möjligheterna att använda flera prototyper i en större skala. Under projektets gång byggdes ett prototypfordon för att genomföra experimenten angående lämpligheten med navigering via infrarött ljus. Tester med prototypen visar att navigering via infrarött ljus är väldigt pålitligt under kontrollerade omständigheter. Projektet utforskar även hur hierarkisk mjukvaruarkitektur står sig mot helt lokal eller centraliserad mjukvaruarkitektur.
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Cowlagi, Raghvendra V. "Hierarchical motion planning for autonomous aerial and terrestrial vehicles." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41066.
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Autonomous mobile robots - both aerial and terrestrial vehicles - have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory.
Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle.
Although simple and efficient, such hierarchical separation suffers a serious drawback: the geometric path planner has no information of the kinematic and dynamic constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates altogether the geometric planner by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption concerning the vehicle kinematic model.
In this thesis, we propose a hierarchical motion planning framework based on a novel mode of interaction between these two levels of planning. This interaction rests on the solution of a special shortest-path problem on graphs, namely, one using costs defined on multiple edge transitions in the path instead of the usual single edge transition costs. These costs are provided by a local trajectory generation algorithm, which we implement using model predictive control and the concept of effective target sets for simplifying the non-convex constraints involved in the problem. The proposed motion planner ensures "consistency" between the two levels of planning, i.e., a guarantee that the higher level geometric path is always associated with a kinematically and dynamically feasible trajectory. We show that the proposed motion planning approach offers distinct advantages in comparison with the competing approaches of discretization of the state space, of randomized sampling-based motion planning, and of local feedback-based, decoupled hierarchical motion planning. Finally, we propose a multi-resolution implementation of the proposed motion planner, which requires accurate descriptions of the environment and the vehicle only for short-term, local motion planning in the immediate vicinity of the vehicle.
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Gooding, Trent R. "A framework for evaluating advanced search concepts for multiple Autonomous Underwater Vehicle (AUV) Mine Countermeasures (MCM)." Thesis, Springfield, Va. : Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA387798.
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Thesis (Degrees of Naval Engineer and M.S. in Ocean Systems Management) Massachusetts Institute of Technology, February 2001.<br>Includes bibliographical references (leafs 113-114). Also Available online.
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Gooding, Trent R. (Trent Ronald) 1972. "A framework for evaluating advanced search concepts for multiple autonomous underwater vehicles (AUV) mine countermeasures (MCM)." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/34341.
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Thesis (Nav.E. and S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, February 2001.<br>Includes bibliographical references (leaves 113-114).<br>Waterborne mines pose an asymmetric threat to naval forces. Their presence, whether actual or perceived, creates a low-cost yet very powerful deterrent that is notoriously dangerous and time consuming to counter. In recent years, autonomous underwater vehicles (AUV) have emerged as a viable technology for conducting underwater search, survey, and clearance operations in support of the mine countermeasures (MCM) mission. With continued advances in core technologies such as sensing, navigation, and communication, future AUV MCM operations are likely to involve many vehicles working together to enhance overall capability. Given the almost endless number of design and configuration possibilities for multiple-AUV MCM systems, it is important to understand the cost-benefit trade-offs associated with these systems. This thesis develops an analytical framework for evaluating advanced AUV MCM system concepts. The methodology is based on an existing approach for naval ship design. For the MCM application, distinct performance and effectiveness metrics are used to describe a series of AUV systems in terms of physical/performance characteristics and then to translate those characteristics into numeric values reflecting the mission-effectiveness of each system. The mission effectiveness parameters are organized into a hierarchy and weighted, using Analytical Hierarchy Process (AHP) techniques, according to the warfighter's preferences for a given operational scenario. Utility functions and modeling provide means of relating the effectiveness metrics to the system-level performance parameters. Implementation of this approach involves two computer-based models: a system model and an effectiveness model, which collectively perform the tasks just described. The evaluation framework is demonstrated using two simple case studies involving notional AUV MCM systems. The thesis conclusion discusses applications and future development potential for the evaluation model.<br>by Trent R. Gooding.<br>Nav.E.and S.M.
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Sjöö, Kristoffer. "Functional understanding of space : Representing spatial knowledge using concepts grounded in an agent's purpose." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-48400.
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This thesis examines the role of function in representations of space by robots - that is, dealing directly and explicitly with those aspects of space and objects in space that serve some purpose for the robot. It is suggested that taking function into account helps increase the generality and robustness of solutions in an unpredictable and complex world, and the suggestion is affirmed by several instantiations of functionally conceived spatial models. These include perceptual models for the "on" and "in" relations based on support and containment; context-sensitive segmentation of 2-D maps into regions distinguished by functional criteria; and, learned predictive models of the causal relationships between objects in physics simulation. Practical application of these models is also demonstrated in the context of object search on a mobile robotic platform.<br>QC 20111125
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Kragelund, Sean P. "Optimal sensor-based motion planning for autonomous vehicle teams." Thesis, Monterey, California: Naval Postgraduate School, 2017. http://hdl.handle.net/10945/53003.
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Approved for public release; distribution is unlimited<br>Reissued 30 May 2017 with correction to student's affiliation on title page.<br>Autonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasures (MCM). A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algo-rithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful frame-work for solving these types of motion planning problems with dynamic constraints and di_x000B_erent performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertainty—generalized optimal control (GenOC)— thus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for di_x000B_erentMCMsearch missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patterns—especially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal mo-tion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.
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Blatt, Florian Andreas [Verfasser]. "The multi-agent flood algorithm as an autonomous system for search and rescue applications / Florian Andreas Blatt." Hannover : Technische Informationsbibliothek (TIB), 2018. http://d-nb.info/1169685013/34.
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Liao, Yan. "Decentralized Decision Making and Information Sharing in a Team of Autonomous Mobile Agents." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353101024.
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Gu, Tianyu. "Improved Trajectory Planning for On-Road Self-Driving Vehicles Via Combined Graph Search, Optimization & Topology Analysis." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/794.
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Trajectory planning is an important component of autonomous driving. It takes the result of route-level navigation plan and generates the motion-level commands that steer an autonomous passenger vehicle (APV). Prior work on solving this problem uses either a sampling-based or optimization-based trajectory planner, accompanied by some high-level rule generation components.
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Kohlbrecher, Stefan [Verfasser], Oskar von [Akademischer Betreuer] Stryk, and Daniele [Akademischer Betreuer] Nardi. "A Holistic Approach for Highly Versatile Supervised Autonomous Urban Search and Rescue Robots / Stefan Kohlbrecher ; Oskar von Stryk, Daniele Nardi." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2016. http://d-nb.info/1120585228/34.
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Acuna, Virgilio. "Using Unmanned Aerial Vehicles for Wireless Localization in Search and Rescue." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3646.
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This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user's location is determined with a 81.8% accuracy.
UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously.
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Bagley, Phil. "Investigations of the behaviour of demersal grenadier fish, Coryphaenoides (Nematonurus) armatus in the abyssal Pacific and Atlantic oceans using an autonomous acoustic tracking vehicle." Thesis, University of Aberdeen, 1993. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU049186.
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The deep-sea has long been perceived as a food limited environment. The recent discovery of seasonal inputs of organic matter into the deep, in areas of the North Pacific and North Atlantic Oceans, has led to questions about the behaviour of deep-sea demersal animals. This thesis describes an autonomous acoustic tracking system (AUDOS: Aberdeen University Deep Ocean Submersible) developed to address the behaviour question; primarily of the demersal grenadier fish, <i>Coryphaenoides (Nematonurus) armatus</i>, but also other shallower living species. Deep-sea acoustic pinger and Code Activated Transponder (CAT) ingestible fish tags, capable of operating at depths of 600m, are described. Deep-sea pingers operated with an early version of AUDOS, returning directional information on fish which ingested tags. The CAT, which is acoustically interrogated by a scanning sonar mounted on AUDOS, returns precise direction and range of individually tagged fish. AUDOS is an autonomous free-fall vehicle incorporating a microprocessor based logging unit which also controls a camera, current meter and compass, enabling several tagged fish to be individually located within a 500m radius. On-board environmental monitoring allows relationships between fish movements and the localised current to be addressed. Three locations were visited during 5 research cruises, Station M (32<SUP>o</SUP>50'N, 122<SUP>o</SUP>50'W) in the North Pacific Ocean, station MAP (29<SUP>o</SUP>30'N, 21<SUP>o</SUP>16'W) in the North Atlantic Ocean and the Porcupine Seabight area (51<SUP>o</SUP>20'N, 14<SUP>o</SUP>0'W) also in the North Atlantic Ocean. Tracking results indicate that <i>C. (N.) armatus</i> adopt an active foraging strategy at each of these stations. Evidence was found for a faster mean swimming speed, for the grenadier, in October (0.0692 m.s<SUP>-1</SUP>) compared to February (0.0262 m.s<SUP>-1</SUP>) at station M. Fish activity is elevated in October following a seasonal enrichment of the benthos by downward flux of particulate organic matter from the surface. At the Continental slope region of the Porcupine Seabight, the grenadier was found to be moving up the slope rather than directly using the current to forage for food (Station MAP).
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Prado, Marcos Gomes. "Planejamento de trajetória para estacionamento de veículos autônomos." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13052013-101339/.
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A navegação autônoma é um dos problemas fundamentais na área de robótica móvel. Esse problema vem sendo pesquisado nessa área por décadas e ainda apresenta um grande potencial para pesquisas científicas. A maior parte dos algoritmos e soluções desenvolvidas nessa área foi concebida para que robôs operem em ambientes estruturados. No entanto, outra questão de grande interesse para pesquisadores da área é a navegação em ambientes externos. Em ambientes não estruturado os veículos autônomos (robôs de grande porte) devem ser capazes de desviar de obstáculos, que eventualmente apareçam no caminho. Esta dissertação aborda o desenvolvimento de um sistema inteligente capaz de gerar e executar um planejamento de caminho para o estacionamento de veículos autônomos em ambientes semi-estruturados. O sistema é capaz de reconhecer vagas de estacionamento por meio de sensores instalados no veículo, gerar uma trajetória válida que o conduza até a vaga e enviar os comandos de esterçamento e aceleração que guiam o veículo pelo caminho gerado<br>Autonomous navigation is one of the fundamental problems in mobile robotics. This problem has been addressed for decades and still has great potential for scientific research. Most solutions and algorithms developed in this field is designed for robots that operate in structured environments. However, another issue of great interest to researchers in this area is autonomous navigation in outdoor environments. In partially structured environments autonomous vehicles (large robots) must be able to avoid obstacles that may arise along the way. This dissertation addresses the development of an intelligent system able to generate and run a path planning for parking of autonomous vehicles in semi-structured environments. The system is able to recognize parking lots using sensors installed in the vehicle, generate a valid path that leads up to the parking lot and send the steering commands and acceleration that to guide the vehicle to its goal point
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Galvez, ramirez Nicolas. "A Framework for Autonomous Generation of Strategies in Satisfiability Modulo Theories Improving complex SMT strategies with learning Optimizing SMT Solving Strategies by Learning with an Evolutionary Process Evolving SMT Strategies Towards Automated Strategies in Satisfiability Modulo Theory." Thesis, Angers, 2018. http://www.theses.fr/2018ANGE0026.
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La génération de stratégies pour les solveurs en Satisfiabilité Modulo des Théories (SMT) nécessite des outils théoriques et pratiques qui permettent aux utilisateurs d’exercer un contrôle stratégique sur les aspects heuristiques fondamentaux des solveurs de SMT, tout en garantissant leur performance. Nous nous intéressons dans cette thèse au solveur Z3 , l’un des plus efficaces lors des compétitions SMT (SMT-COMP). Dans les solveurs SMT, la définition d’une stratégie repose sur un ensemble de composants et paramètres pouvant être agencés et configurés afin de guider la recherche d’une preuve de (in)satisfiabilité d’une instance donnée. Dans cette thèse, nous abordons ce défi en définissant un cadre pour la génération autonome de stratégies pour Z3, c’est-à-dire un algorithme qui permet de construire automatiquement des stratégies sans faire appel à des connaissances d’expertes. Ce cadre général utilise une approche évolutionnaire (programmation génétique), incluant un système à base de règles. Ces règles formalisent la modification de stratégies par des principes de réécriture, les algorithmes évolutionnaires servant de moteur pour les appliquer. Cette couche intermédiaire permettra d’appliquer n’importe quel algorithme ou opérateur sans qu’il soit nécessaire de modifier sa structure, afin d’introduire de nouvelles informations sur les stratégies. Des expérimentations sont menées sur les jeux classiques de la compétition SMT-COMP<br>The Strategy Challenge in Satisfiability Modulo Theories (SMT) claims to build theoretical and practical tools allowing users to exert strategic control over core heuristic aspects of high-performance SMT solvers. In this work, we focus in Z3 Theorem Prover: one of the most efficient SMT solver according to the SMT Competition, SMT-COMP. In SMT solvers, the definition of a strategy relies on a set of tools that can be scheduled and configured in order to guide the search for a (un)satisfiability proof of a given instance. In this thesis, we address the Strategy Challenge in SMT defining a framework for the autonomous generation of strategies in Z3, i.e. a practical system to automatically generate SMT strategies without the use of expert knowledge. This framework is applied through an incremental evolutionary approach starting from basic algorithms to more complex genetic constructions. This framework formalise strategies modification as rewriting rules, where algorithms acts as enginess to apply them. This intermediate layer, will allow apply any algorithm or operator with no need to being structurally modified, in order to introduce new information in strategies. Validation is done through experiments on classic benchmarks of the SMT-COMP
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Neves, Vânia de Oliveira. "Automatização do teste estrutural de software de veículos autônomos para apoio ao teste de campo." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-15092015-090805/.
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Veículo autônomo inteligente (ou apenas veículo autônomo VA) é um tipo de sistema embarcado que integra componentes físicos (hardware) e computacionais (software). Sua principal característica é a capacidade de locomoção e de operação de modo semi ou completamente autônomo. A autonomia cresce com a capacidade de percepção e de deslocamento no ambiente, robustez e capacidade de resolver e executar tarefas lidando com as mais diversas situações (inteligência). Veículos autônomos representam um tópico de pesquisa importante e que tem impacto direto na sociedade. No entanto, à medida que esse campo avança alguns problemas secundários aparecem como, por exemplo, como saber se esses sistemas foram suficientemente testados. Uma das fases do teste de um VA é o teste de campo, em que o veículo é levado para um ambiente pouco controlado e deve executar livremente a missão para a qual foi programado. Ele é geralmente utilizado para garantir que os veículos autônomos mostrem o comportamento desejado, mas nenhuma informação sobre a estrutura do código é utilizada. Pode ocorrer que o veículo (hardware e software) passou no teste de campo, mas trechos importantes do código nunca tenham sido executados. Durante o teste de campo, os dados de entrada são coletados em logs que podem ser posteriormente analisados para avaliar os resultados do teste e para realizar outros tipos de teste offline. Esta tese apresenta um conjunto de propostas para apoiar a análise do teste de campo do ponto de vista do teste estrutural. A abordagem é composta por um modelo de classes no contexto do teste de campo, uma ferramenta que implementa esse modelo e um algoritmo genético para geração de dados de teste. Apresenta também heurísticas para reduzir o conjunto de dados contidos em um log sem diminuir substancialmente a cobertura obtida e estratégias de combinação e mutação que são usadas no algoritmo. Estudos de caso foram conduzidos para avaliar as heurísticas e estratégias e são também apresentados e discutidos.<br>Intelligent autonomous vehicle (or just autonomous vehicle - AV) is a type of embedded system that integrates physical (hardware) and computational (software) components. Its main feature is the ability to move and operate partially or fully autonomously. Autonomy grows with the ability to perceive and move within the environment, robustness and ability to solve and perform tasks dealing with different situations (intelligence). Autonomous vehicles represent an important research topic that has a direct impact on society. However, as this field progresses some secondary problems arise, such as how to know if these systems have been sufficiently tested. One of the testing phases of an AV is the field testing, where the vehicle is taken to a controlled environment and it should execute the mission for which it was programed freely. It is generally used to ensure that autonomous vehicles show the intended behavior, but it usually does not take into consideration the code structure. The vehicle (hardware and software) could pass the field testing, but important parts of the code may never have been executed. During the field testing, the input data are collected in logs that can be further analyzed to evaluate the test results and to perform other types of offline tests. This thesis presents a set of proposals to support the analysis of field testing from the point of view of the structural testing. The approach is composed of a class model in the context of the field testing, a tool that implements this model and a genetic algorithm to generate test data. It also shows heuristics to reduce the data set contained in a log without reducing substantially the coverage obtained and combination and mutation strategies that are used in the algorithm. Case studies have been conducted to evaluate the heuristics and strategies, and are also presented and discussed.
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Kowadlo, Gideon. "Robot odour localisation in enclosed and cluttered environments using naïve physics." Monash University. Faculty of Engineering. Department of Electrical and Computer Systems Engineering, 2007. http://arrow.monash.edu.au/hdl/1959.1/53471.
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Odour localisation is the problem of finding the source of an odour or other volatile chemical. It promises many valuable practical and humanitarian applications. Most localisation methods require a robot to reactively track an odour plume along its entire length. This approach is time consuming and may be not be possible in a cluttered indoor environment, where airflow tends to form sectors of circulating airflow. Such environments may be encountered in crawl-ways under floors, roof cavities, mines, caves, tree-canopies, air-ducts, sewers or tunnel systems. Operation in these places is important for such applications as search and rescue and locating the sources of toxic chemicals in an industrial setting. This thesis addresses odour localisation in this class of environments. The solution consists of a sense-map-plan-act style control scheme (and low level behaviour based controller) with two main stages. Firstly, the airflow in the environment is modelled using naive physics rules which are encapsulated into an algorithm named a Naive Reasoning Machine. It was used in preference to conventional methods as it is fast, does not require boundary conditions, and most importantly, provides approximate solutions to the degree of accuracy required for the task, with analogical data structures that are readily useful to a reasoning algorithm. Secondly, a reasoning algorithm navigates the robot to specific target locations that are determined with a physical map, the airflow map, and knowledge of odour dispersal. Sensor measurements at the target positions provide information regarding the likelihood that odour was emitted from potential odour source locations. The target positions and their traversal are determined so that all the potential odour source sites are accounted for. The core method provides values corresponding to the confidence that the odour source is located in a given region. A second search stage exploiting vision is then used to locate the specific location of the odour source within the predicted region. This comprises the second part of a bi-modal, two-stage search, with each stage exploiting complementary sensing modalities. Single hypothesis airflow modelling faces limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. A method is presented for dealing with these uncertainties, by generating multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. This method improves the robustness of odour localisation in the presence of uncertainties, making it possible where the single hypothesis method would fail. It also demonstrates the potential for integrating naive physics into a statistical framework. Extensive experimental results are presented to support the methods described above.
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Chatzilygeroudis, Konstantinos. "Micro-Data Reinforcement Learning for Adaptive Robots." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0276/document.
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Les robots opèrent dans le monde réel, dans lequel essayer quelque chose prend beaucoup de temps. Pourtant, les methodes d’apprentissage par renforcement actuels (par exemple, deep reinforcement learning) nécessitent de longues périodes d’interaction pour trouver des politiques efficaces. Dans cette thèse, nous avons exploré des algorithmes qui abordent le défi de l’apprentissage par essai-erreur en quelques minutes sur des robots physiques. Nous appelons ce défi “Apprentissage par renforcement micro-data”. Dans la première contribution, nous avons proposé un nouvel algorithme d’apprentissage appelé “Reset-free Trial-and-Error” qui permet aux robots complexes de s’adapter rapidement dans des circonstances inconnues (par exemple, des dommages) tout en accomplissant leurs tâches; en particulier, un robot hexapode endommagé a retrouvé la plupart de ses capacités de marche dans un environnement avec des obstacles, et sans aucune intervention humaine. Dans la deuxième contribution, nous avons proposé un nouvel algorithme de recherche de politique “basé modèle”, appelé Black-DROPS, qui: (1) n’impose aucune contrainte à la fonction de récompense ou à la politique, (2) est aussi efficace que les algorithmes de l’état de l’art, et (3) est aussi rapide que les approches analytiques lorsque plusieurs processeurs sont disponibles. Nous avons aussi proposé Multi-DEX, une extension qui s’inspire de l’algorithme “Novelty Search” et permet de résoudre plusieurs scénarios où les récompenses sont rares. Dans la troisième contribution, nous avons introduit une nouvelle procédure d’apprentissage du modèle dans Black-DROPS qui exploite un simulateur paramétré pour permettre d’apprendre des politiques sur des systèmes avec des espaces d’état de grande taille; par exemple, cette extension a trouvé des politiques performantes pour un robot hexapode (espace d’état 48D et d’action 18D) en moins d’une minute d’interaction. Enfin, nous avons exploré comment intégrer les contraintes de sécurité, améliorer la robustesse et tirer parti des multiple a priori en optimisation bayésienne. L'objectif de la thèse était de concevoir des méthodes qui fonctionnent sur des robots physiques (pas seulement en simulation). Par conséquent, tous nos approches ont été évaluées sur au moins un robot physique. Dans l’ensemble, nous proposons des méthodes qui permettre aux robots d’être plus autonomes et de pouvoir apprendre en poignée d’essais<br>Robots have to face the real world, in which trying something might take seconds, hours, or even days. Unfortunately, the current state-of-the-art reinforcement learning algorithms (e.g., deep reinforcement learning) require big interaction times to find effective policies. In this thesis, we explored approaches that tackle the challenge of learning by trial-and-error in a few minutes on physical robots. We call this challenge “micro-data reinforcement learning”. In our first contribution, we introduced a novel learning algorithm called “Reset-free Trial-and-Error” that allows complex robots to quickly recover from unknown circumstances (e.g., damages or different terrain) while completing their tasks and taking the environment into account; in particular, a physical damaged hexapod robot recovered most of its locomotion abilities in an environment with obstacles, and without any human intervention. In our second contribution, we introduced a novel model-based reinforcement learning algorithm, called Black-DROPS that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. We additionally proposed Multi-DEX, a model-based policy search approach, that takes inspiration from novelty-based ideas and effectively solved several sparse reward scenarios. In our third contribution, we introduced a new model learning procedure in Black-DROPS (we call it GP-MI) that leverages parameterized black-box priors to scale up to high-dimensional systems; for instance, it found high-performing walking policies for a physical damaged hexapod robot (48D state and 18D action space) in less than 1 minute of interaction time. Finally, in the last part of the thesis, we explored a few ideas on how to incorporate safety constraints, robustness and leverage multiple priors in Bayesian optimization in order to tackle the micro-data reinforcement learning challenge. Throughout this thesis, our goal was to design algorithms that work on physical robots, and not only in simulation. Consequently, all the proposed approaches have been evaluated on at least one physical robot. Overall, this thesis aimed at providing methods and algorithms that will allow physical robots to be more autonomous and be able to learn in a handful of trials
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Paiva, ?verton de Oliveira. "Melhoria na converg?ncia do algoritmo Q-Learning na aplica??o de sistemas tutores inteligentes." UFVJM, 2016. http://acervo.ufvjm.edu.br/jspui/handle/1/1386.
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Previous issue date: 2016<br>O uso sistemas computacionais como complemento ou substitui??o da sala de aula ? cada vez
mais comum na educa??o e os Sistemas Tutores Inteligentes (STIs) s?o uma dessas alternativas.
Portanto ? fundamental desenvolver STIs capazes tanto de ensinar quanto aprender informa??es
relevantes sobre o aluno atrav?s de t?cnicas de intelig?ncia artificial. Esse aprendizado acontece
por meio da intera??o direta entre o STI e o aluno que ? geralmente demorada. Esta disserta??o
apresenta a inser??o da metaheur?sticas Lista Tabu e GRASP com o objetivo de acelerar esse
aprendizado. Para avaliar o desempenho dessa modifica??o, foi desenvolvido um simulador de
STI. Nesse sistema, foram realizadas simula??es computacionais para comparar o desempenho
da tradicional pol?tica de explora??o aleat?ria e as metaheur?sticas propostas Lista Tabu e
GRASP. Os resultados obtidos atrav?s dessas simula??es e os testes estat?sticos aplicados
indicam fortemente que a introdu??o de meta-heur?sticas adequadas melhoram o desempenho
do algoritmo de aprendizado em STIs.<br>Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016.<br>Using computer systems as a complement or replacement for the classroom experience is an
increasingly common practice in education and Intelligent Tutoring Systems (ITS) are one of
these alternatives. Therefore, it is crucial to develop ITS that are capable of both teaching and
learning relevant information about the student through artificial intelligence techniques. This
learning process occurs by means of direct, and generally slow, interaction between the ITS and
the student. This dissertation presents the insertion of meta-heuristic Tabu search and GRASP
with the purpose of accelera ting learning. An ITS simulator was developed to evaluate the
performance of this change. Computer simulations were conducted in order to compare the
performance of traditional randomized search methods with the meta-heuristic Tabu search.
Results obtained from these simulations and statistical tests strongly indicate that the
introduction of meta-heuristics in exploration policy improves the performance of the learning
algorithm in ITS.
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Page, Jennifer Lynn. "The effects of plume property variation on odor plume navigation in turbulent boundary layer flows." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29752.
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Thesis (Ph.D)--Biology, Georgia Institute of Technology, 2009.<br>Committee Chair: Weissburg, Marc; Committee Member: Hay, Mark; Committee Member: Kubanek, Julia; Committee Member: Webster, Donald; Committee Member: Yen, Jeannette. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Morgan, Hayden Matthew. "Small-Target Detection and Observation with Vision-Enabled Fixed-Wing Unmanned Aircraft Systems." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/8998.
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This thesis focuses on vision-based detection and observation of small, slow-moving targets using a gimballed fixed-wing unmanned aircraft system (UAS). Generally, visual tracking algorithms are tuned to detect motion of relatively large objects in the scene with noticeably significant motion; therefore, applications such as high-altitude visual searches for human motion often ignore target motion as noise. Furthermore, after a target is identified, arbitrary maneuvers for transitioning to overhead orbits for better observation may result in temporary or permanent loss of target visibility. We present guidelines for tuning parameters of the Visual Multiple Target Tracking (Visual MTT) algorithm to enhance its detection capabilities for very small, slow-moving targets in high-resolution images. We show that the tuning approach is able to detect walking motion of a human described by 10-15 pixels from high altitudes. An algorithm is then presented for defining rotational bounds on the controllable degrees of freedom of an aircraft and gimballed camera system for maintaining visibility of a known ground target. Critical rotations associated with the fastest loss or acquisition of target visibility are also defined. The accuracy of these bounds are demonstrated in simulation and simple applications of the algorithm are described for UAS. We also present a path planning and control framework for defining and following both dynamically and visually feasibly transition trajectories from an arbitrary point to an orbit over a known target for further observation. We demonstrate the effectiveness of this framework in maintaining constant target visibility while transitioning to the intended orbit as well as in transitioning to a lower altitude orbit for more detailed visual analysis of the intended target.