Relevant bibliographies by topics / Adaptivive trajectory planning

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Adaptivive trajectory planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Adaptivive trajectory planning"

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Wang, Xin, Junwei Wang, and Zhi Rao. "An adaptive parametric interpolator for trajectory planning." Advances in Engineering Software 41, no. 2 (February 2010): 180–87. http://dx.doi.org/10.1016/j.advengsoft.2009.09.010.

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Wang, Yuan, Zhenglei Wei, Changqiang Huang, Hanqiao Huang, Kexin Zhao, and Cong Li. "Online Cooperative Trajectory Planning for UCAV Formation in Uncertain Environment." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 6 (December 2018): 1145–55. http://dx.doi.org/10.1051/jnwpu/20183661145.

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This paper presents a local online planning method based on hp adaptive pseudospectral method to address the cooperative trajectory planning of UCAV formation in uncertain environment. First, through analyzing attacking process of UCAV formation, the cooperative trajectory planning model is built up by taking UCAV threat, task time and the number of collision and the error of ordered time as the object function. Second, in order to solve the online cooperative trajectory planning, according to real-time environment information and offline planning data, the local planning method based on hp adaptive pseudospectral method is proposed during optimizing time slice. Last but not least, the simulation results for offline and online cooperative trajectory show that the proposed method is valid and can provide cooperative trajectory with high precise control and state information.
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Vu, Nga Thi-Thuy, Nam Phuong Tran, and Nam Hoai Nguyen. "Adaptive Neuro-Fuzzy Inference System Based Path Planning for Excavator Arm." Journal of Robotics 2018 (December 2, 2018): 1–7. http://dx.doi.org/10.1155/2018/2571243.

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This paper presents a scheme based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to generate trajectory for excavator arm. Firstly, the trajectory is predesigned with some specific points in the work space to meet the requirements about the shape. Next, the inverse kinematic is used and optimization problems are solved to generate the via-points in the joint space. These via-points are used as training set for ANFIS to synthesis the smooth curve. In this scheme, the outcome trajectory satisfies the requirements about both shape and optimization problems. Moreover, the algorithm is simple in calculation as the numbers of via-points are large. Finally, the simulation is done for two cases to test the effect of ANFIS structure on the generated trajectory. The simulation results demonstrate that, by using suitable structure of ANFIS, the proposed scheme can build the smooth trajectory which has the good matching with desired trajectory even that the desired trajectory has the complicated shape.
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Kim, Kijung, Youngsoo Kim, Jongwon Kim, Hwa Soo Kim, and Taewon Seo. "Optimal Trajectory Planning for 2-DOF Adaptive Transformable Wheel." IEEE Access 8 (2020): 14452–59. http://dx.doi.org/10.1109/access.2020.2966767.

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Shin, Jin-Ho, and Ju-Jang Lee. "Trajectory planning and robust adaptive control for underactuated manipulators." Electronics Letters 34, no. 17 (1998): 1705. http://dx.doi.org/10.1049/el:19981191.

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Wei, Zhenglei, Changqiang Huang, Dali Ding, Hanqiao Huang, and Huan Zhou. "UCAV Formation Online Collaborative Trajectory Planning Using hp Adaptive Pseudospectral Method." Mathematical Problems in Engineering 2018 (October 22, 2018): 1–25. http://dx.doi.org/10.1155/2018/3719762.

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In this paper, a novel approach to solving the formation online collaborative trajectory planning for fixed-wing Unmanned Combat Aerial Vehicles (UCAVs) is proposed. In order to describe the problem, the formation attack process which consists of communication framework and synergy elements is analyzed. The collaborative trajectory planning model which is based on avoiding the threat zones, reducing the execution time, and accomplishing the mission combines kinematics/dynamics model of UCAV with formation relative motion model to establish the optimal control problem. The approach based on hp adaptive pseudospectral method is presented to generate formation trajectory that satisfies the collaborative constraints. When a trigger event is detected, based on the offline planning, the online collaborative trajectory replanning using rolling horizon strategy is carried out. Simulated experiments which are divided into offline scenarios and online scenarios demonstrate that the proposed approach can generate trajectories which can meet the actual flight constraints, and the results verify the feasibility and stability of the proposed approach.
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ZHANG, HE, RUI WU, CHANGLE LI, XIZHE ZANG, YANHE ZHU, HONGZHE JIN, XUEHE ZHANG, and JIE ZHAO. "ADAPTIVE MOTION PLANNING FOR HITCR-II HEXAPOD ROBOT." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740040. http://dx.doi.org/10.1142/s0219519417400401.

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Multi-legged robots have the ability to traverse rugged terrain and can surmount the obstacles, which are impossible for being overcome by wheeled robots. In this regard, six-legged (hexapod) robots are considered to provide the best combination of adequate adaptability and control complexity. Their motion planning envisages calculating sequences of footsteps and body posture, accounting for the influence of terrain shape, in order to produce the appropriate foot-end trajectory and ensure stable and flexible motion of hexapod robots on the rugged terrain. In this study, a high-order polynomial is used to describe the trajectory model, and a new motion planning theory is proposed, which is aimed at the adaptation of hexapod robots to more complex terrains. An attempt is made to elaborate the adaptive motion planning and perform its experimental verification for a novel hexapod robot HITCR-II, demonstrating its applicability for walking on the unstructured terrain.
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Marti, K. "Stochastic Programming Methods in Adaptive Optimal Trajectory Planning for Robots." ZAMM 82, no. 11-12 (November 2002): 795–809. http://dx.doi.org/10.1002/1521-4001(200211)82:11/12<795::aid-zamm795>3.0.co;2-i.

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Bureerat, Sujin, Nantiwat Pholdee, Thana Radpukdee, and Papot Jaroenapibal. "Self-adaptive MRPBIL-DE for 6D robot multiobjective trajectory planning." Expert Systems with Applications 136 (December 2019): 133–44. http://dx.doi.org/10.1016/j.eswa.2019.06.033.

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Kim, Junsoo, Kichun Jo, Wonteak Lim, and Myoungho Sunwoo. "A probabilistic optimization approach for motion planning of autonomous vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 5 (August 16, 2017): 632–50. http://dx.doi.org/10.1177/0954407017704782.

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This paper presents a novel probabilistic approach for improving the motion planning performance of autonomous driving. The proposed approach is based on the sampling-based planning algorithm, which generates an optimal trajectory from a set of trajectory candidates. In order to treat the uncertainty in the perception data and the vehicle system, the particle filter framework is applied to the motion planning algorithm with four main steps: the time update of the trajectory candidates, the perception measurement update, the trajectory selection and the motion goal resampling. Since the proposed planning algorithm recursively generates an optimal trajectory, the time update of the trajectory candidate updates the motion goals of the trajectory candidates in the previous step using the vehicle model, and it also generates a new set of candidates. In order to evaluate the optimality of each candidate with regard to the safety and the reliability, a perception measurement update is performed. In this step, the importance weight of each candidate is computed using perception data and its adaptive likelihood function. Based on the candidates with updated importance weights, an optimal trajectory is determined in the trajectory selection. Then, the motion goal resampling modifies the set of motion goals based on the importance weights for efficient management of the motion goals in the iterative planning algorithm. The developed algorithm is validated using various types of test. The results show that the proposed method not only provides an integrated probabilistic interface between the perception and the planning but also results in an excellent performance in terms of the computation efficiency.
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Dissertations / Theses on the topic "Adaptivive trajectory planning"

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Dizorzi, Matúš. "Adaptivní plánování trajektorie průmyslového robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400667.

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This thesis deals with the extension of the RoScan scanning system features, making its behaviour more secure and adaptivte during scanning of the object on its whole trajectory. This work contains mathematical model of said manipulator, suggested methods to ensure proper behaviour during singularities. New features were added to the RoScan system such as control panel for manipulator control including new format of trajectory log, moving closer or further away from manipulator’s end effector and non adaptive trajectory testing for singularities. Result of this work is ready-to-use.
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Aboud, Vieider Felicia, and Anirudh Narasimha Kulkarni. "Traction Adaptive trajectory planning for autonomous racing." Thesis, KTH, Maskinkonstruktion (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281249.

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The autonomous driving industry has undergone leaps and bounds of research to reach the mainstream market, with major players investing heavily to improve the technology further. Industry and academia are currently working to make the technology safe, reliable and robust. Autonomous racing provides this opportunity, to improve the technology to the point, where it can utilize the full physical capability of the vehicle in a wide range of operational conditions. Multiple functionalities are required to make the car autonomous, this thesis focuses on the path planning module for autonomous racing. We evaluated the performance of dynamic models, using different adaptive dynamic constraints, implemented for path planning. The evaluation is based on framework for optimization based motion planning[1] The optimization problem is solved by "Sampling Augmented Real Time Iteration (SAARTI) motion planning scheme". Four different models were studied during this thesis and include dynamic bicycle models, with static and dynamic constraints. Parameters affecting the planning performance were identified, and the trade-off between model complexity and planning horizon, was investigated by varying these parameters and the differences in performance was studied. The generalizability of results for different driving conditions was investigated for these parameter configurations. Batch simulations were performed to account for various possible scenarios of different parameter configurations, to ensure results closest to reality. The simulation was conducted with, hardware in the loop setup running the planning node, to get a realistic estimation of the computation resources. Batch simulations were instrumental in showing interesting trends of how the input parameters affected the planning performance. Simulations provided extensive proof of the different dynamic constraints improving the planning performance, over the basic dynamic model under extreme driving conditions. When reviewing the results from the simulations, the traction adaptive model showed robust and consistent performance. The results showed that combining friction estimation and load adaption, increased the ability to plan for local variations, reduced failure and improved laptime. When designing a planner for a race car that is exposed for local variations, the friction estimation is needed to reduce errors and the load adaption is needed for better handling of the vehicle to perform critical maneuvers.
De senaste åren har den autonoma fordons industrin genomgått en stor utveckling genom forskning, företag har gjort stora investeringar för att förbättra teknologin för att kunna nå den privata marknaden. Industrin och akademin jobbar fortfarande för att göra autonoma bilar säkra, pålitliga och robusta. Autonom racing tillhandahåller en plattform för att förbättra tekniken så att den kan utnyttja fordonets fulla fysiska förmåga i ett brett spektrum av driftsförhållanden. Flera funktioner krävs för att göra bilen autonom, detta arbete fokuserar på rörelseplaneringsmodulen för autonom racing. Vi har utvärderat hur rörelseplanerings algoritmen presterar vid användning av en dynamisk modell med dynamiska begräsningar. Utvärderingen är baserad på ett ramverk för optimal rörelseplanering [1] vilken löser optimerings problemet genom användning av "Sampling Augmented Real Time Iteration (SAARTI) motion planning scheme". Fyra olika modeller jämfördes vilka inkluderade en dynamisk cykelmodell med både statiska och dynamiska begränsningar. De parametrar som påverkade prestandan identifierades, och avvägningen mellan modell komplexitet och planerings horisont undersöktes genom att studera skillnader i prestanda för olika parameter konfigurationer. Generaliserbarhet av resultaten undersöktes genom att studera prestandan för olika parameter konfigurationer under olika körförhållanden. Batch simuleringar utfördes för att ta hänsyn till många olika scenarion, för att säkerställa att resultaten var så nära verkligheten som möjligt. Simuleringarna visade att användning av dynamiska begränsningar vid rörelse planering förbättrar prestandan jämfört med att använda statiska begränsningar vid extrema körförhållanden. Observation av resultaten från simuleringarna visade att användning av den grepp adaptiva modellen resulterade i robust och konsistent prestanda. Att kombinera estimering av friktion och samtidigt ta hänsyn till en varierande normal kraft, ökar förmågan att planera för variationer i friktion, minskar chansen att bilen kör av vägen och förbättrar varvtiden.
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Anisi, David A. "Online trajectory planning and observer based control." Licentiate thesis, Stockholm : Optimization and systems theory, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4153.

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Albagul, Abdulgani. "Dynamic modelling and control of a wheeled mobile robot." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327239.

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J'alics, Laci. "Trajectory planning for terrain adaptive locomotion and rhythmic movements of a neuromuscular biped /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487941504296459.

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Shui, Yuhao. "Strategic Trajectory Planning of Highway Lane Change Maneuver with Longitudinal Speed Control." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1431093441.

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Anisi, David A. "On Cooperative Surveillance, Online Trajectory Planning and Observer Based Control." Doctoral thesis, KTH, Optimeringslära och systemteori, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9990.

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The main body of this thesis consists of six appended papers. In the  first two, different  cooperative surveillance problems are considered. The second two consider different aspects of the trajectory planning problem, while the last two deal with observer design for mobile robotic and Euler-Lagrange systems respectively.In Papers A and B,  a combinatorial optimization based framework to cooperative surveillance missions using multiple Unmanned Ground Vehicles (UGVs) is proposed. In particular, Paper A  considers the the Minimum Time UGV Surveillance Problem (MTUSP) while Paper B treats the Connectivity Constrained UGV Surveillance Problem (CUSP). The minimum time formulation is the following. Given a set of surveillance UGVs and a polyhedral area, find waypoint-paths for all UGVs such that every point of the area is visible from  a point on a waypoint-path and such that the time for executing the search in parallel is minimized.  The connectivity constrained formulation  extends the MTUSP by additionally requiring the induced information graph to be  kept recurrently connected  at the time instants when the UGVs  perform the surveillance mission.  In these two papers, the NP-hardness of  both these problems are shown and decomposition techniques are proposed that allow us to find an approximative solution efficiently in an algorithmic manner.Paper C addresses the problem of designing a real time, high performance trajectory planner for an aerial vehicle that uses information about terrain and enemy threats, to fly low and avoid radar exposure on the way to a given target. The high-level framework augments Receding Horizon Control (RHC) with a graph based terminal cost that captures the global characteristics of the environment.  An important issue with RHC is to make sure that the greedy, short term optimization does not lead to long term problems, which in our case boils down to two things: not getting into situations where a collision is unavoidable, and making sure that the destination is actually reached. Hence, the main contribution of this paper is to present a trajectory planner with provable safety and task completion properties. Direct methods for trajectory optimization are traditionally based on a priori temporal discretization and collocation methods. In Paper D, the problem of adaptive node distribution is formulated as a constrained optimization problem, which is to be included in the underlying nonlinear mathematical programming problem. The benefits of utilizing the suggested method for  online  trajectory optimization are illustrated by a missile guidance example.In Paper E, the problem of active observer design for an important class of non-uniformly observable systems, namely mobile robotic systems, is considered. The set of feasible configurations and the set of output flow equivalent states are defined. It is shown that the inter-relation between these two sets may serve as the basis for design of active observers. The proposed observer design methodology is illustrated by considering a  unicycle robot model, equipped with a set of range-measuring sensors. Finally, in Paper F, a geometrically intrinsic observer for Euler-Lagrange systems is defined and analyzed. This observer is a generalization of the observer proposed by Aghannan and Rouchon. Their contractivity result is reproduced and complemented  by  a proof  that the region of contraction is infinitely thin. Moreover, assuming a priori bounds on the velocities, convergence of the observer is shown by means of Lyapunov's direct method in the case of configuration manifolds with constant curvature.
QC 20100622
TAIS, AURES
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Bose, A. Subhash Chandra. "Adaptive trajectory planning for industrial robots." 1987. http://catalog.hathitrust.org/api/volumes/oclc/16405056.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1987.
Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 116-132).
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Lee, Chi-Tai, and 李啟泰. "Trajectory Planning and Adaptive Trajectory Tracking Control for a Small Scale Autonomous Helicopter." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/35376378022379391196.

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博士
國立中興大學
電機工程學系所
98
This dissertation presents three nonlinear adaptive trajectory tracking controllers as well as an on-line trajectory generation method for a small scale autonomous helicopter. The proposed trajectory tracking controllers are mainly on the basis of the adaptive backstepping design technique with an integral action. Unlike those approximate modeling approaches neglecting the nonlinear coupling terms among force equations, the developments of three proposed controllers are intentionally based on the complete rigid-body model such that the closed-loop helicopter systems are guaranteed to be semi-globally ultimately bounded and have satisfactory trajectory tracking performance over its entire flight envelope. Three different adaptive techniques are used to cope with the coupling terms existing in the force equations of the complete rigid-body model. In particular, RBFNN and RNN are adopted to accommodate the adaptive backstepping integral scheme with an augmented approximation function and robust performance respectively. Furthermore, the local path generation based on the elastic band concept is proposed to find an on-line collision-free trajectory for the tracking controller of a small scale helicopter. In addition to the complete evolution of synthesis process and stability analysis, the proposed controllers are verified by using a software-in-the-loop approach which implements a high fidelity dynamic model of a small-scale helicopter. The effectiveness and merits of the proposed methods are exemplified by conducting several dynamic simulations, including specified maneuvers of hovering and trajectory tracking, autonomous tasks of obstacle avoidance, and terrain following.
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Wang, Meng-ko, and 王夢柯. "Fuzzy Adaptive Control for Hexapod Robots with Trajectory Planning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fhj4pt.

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碩士
大同大學
電機工程學系(所)
106
Trajectory planning and tracking control are important for the robotics research to achieve the locomotion of the hexapod robots. This thesis proposes the hexapod robot locomotion control using fuzzy tracking control design algorithm. The human-machine interface including kinematics and inverse kinematics written by C$\#$ in Visual Studio is used for obtaining the joint angles corresponding to the desired trajectory. Given the desired trajectory, the coordinates of the center of mass (COM) of the hexapod robot is given through the conversion transformation matrix between the coordinates of the COM and the coordinate systems of legs. Then, these coordinates to track the desired trajectory will be mapped to the joints for each leg by the inverse kinematics. In addition, the stability of the closed-loop control system for the fuzzy adaptive control algorithm and trajectory planning is guaranteed by the Lyapunov theorem, and the robots can achieve trajectory planning and tracking. The experiments demonstrate that the proposed control scheme can work effectively tracking the desired trajectory in balancing.
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Book chapters on the topic "Adaptivive trajectory planning"

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Aurnhammer, Andreas, and Kurt Marti. "Adaptive Optimal Stochastic Trajectory Planning." In Online Optimization of Large Scale Systems, 521–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04331-8_27.

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Marti, Kurt. "Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC)." In Stochastic Optimization Methods, 119–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46214-0_4.

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Marti, K. "Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC) for Robots." In Lecture Notes in Economics and Mathematical Systems, 155–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-55884-9_9.

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Arora, Akash, P. Michael Furlong, Robert Fitch, Terry Fong, Salah Sukkarieh, and Richard Elphic. "Online Multi-modal Learning and Adaptive Informative Trajectory Planning for Autonomous Exploration." In Field and Service Robotics, 239–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67361-5_16.

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Zou, Qingxiao, Weidong Guo, and Fouaz Younès Hamimid. "A Novel Robot Trajectory Planning Algorithm Based on NURBS Velocity Adaptive Interpolation." In Advances in Mechanical Design, 1191–208. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6553-8_78.

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Zhai, Jingmei, Kun Liu, Haiyang He, and Fan Ouyang. "An Efficient Approach for Collision-Free Trajectory Planning Using Adaptive Velocity Vector Field Algorithm." In Advances in Mechanical Design, 1169–90. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6553-8_77.

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Roy, Abhishek Ghosh, and Pratyusha Rakshit. "Motion Planning of Non-Holonomic Wheeled Robots Using Modified Bat Algorithm." In Nature-Inspired Algorithms for Big Data Frameworks, 94–123. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5852-1.ch005.

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The chapter proposes a novel optimization framework to solve the motion planning problem of non-holonomic wheeled mobile robots using swarm algorithms. The specific robotic system considered is a vehicle with approximate kinematics of a car. The configuration of this robot is represented by position and orientation of its main body in the plane and by angles of the steering wheels. Two control inputs are available for motion control including the velocity and the steering angle command. Moreover, the car-like robot is one of the simplest non-holonomic vehicles that displays the general characteristics and constrained maneuverability of systems with non-holonomicity. The control methods proposed in this chapter do not require precise mathematical modeling of every aspect a car-like system. The swarm algorithm-based motion planner determines the optimal trajectory of multiple car-like non-holonomic robots in a given arena avoiding collision with obstacles and teammates. The motion planning task has been taken care of by an adaptive bio-inspired strategy commonly known as Bat Algorithm.
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Vera, S., F. Petric, G. Heredia, A. Ollero, and Z. Kovacic. "Trajectory Planning Based on Collocation Methods for Adaptive Motion Control of Multiple Aerial and Ground Autonomous Vehicles." In Control of Complex Systems, 585–634. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-805246-4.00021-5.

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Conference papers on the topic "Adaptivive trajectory planning"

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Dieumegard, Pierre, Supatcha Chaimatanan, and Daniel Delahaye. "Large Scale Adaptive 4D Trajectory Planning." In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC). IEEE, 2018. http://dx.doi.org/10.1109/dasc.2018.8569632.

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Alonso-Portillo, I., and E. Atkins. "Adaptive trajectory planning for flight management systems." In 40th AIAA Aerospace Sciences Meeting & Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1073.

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Lee, Ritchie, Javier Puig - Navarro, Adrian K. Agogino, Dimitra Giannakoupoulou, Ole J. Mengshoel, Mykel J. Kochenderfer, and B. Danette Allen. "Adaptive Stress Testing of Trajectory Planning Systems." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-1454.

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Fotouhi-C., Reza, Peter N. Nikiforuk, and Walerian Szyszkowski. "Combined Trajectory Planning and Parameter Identification of a Two-Link Rigid Manipulator." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/mech-5994.

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Abstract A combined trajectory planning problem and adaptive control problem for a two-link rigid manipulator is presented in this paper. The problem is divided into two parts: path planning for off-line processing, followed by on-line path tracking using an adaptive controller. The path planning is done at the joint level. The motion of the robot is specified by a sequence of knots (positions of the robot’s tip) in space Cartesian coordinates. These knots are then transformed into two sets of joint displacements, and piecewise cubic polynomials are used to fit these two sequences of joint displacements. The cubic spline function is used to construct a trajectory with the velocity and the acceleration as constraints. Linear scaling of the time variable is used to accommodate the velocity and acceleration constraints. A nonlinear scaling of the time variable is performed to fit the velocity to a pre-specified velocity profile. The adaptive scheme used takes full advantage of the known parameters of the manipulator while estimating the unknown parameters. In deriving the dynamic equations of motion, all of the physical parameters of the manipulator, including the distributed masses of the links, are taken into account. Some simulation results for the manipulator with unknown payload masses following a planned trajectory are presented.
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Evans, Ethan N., Patrick Meyer, Samuel Seifert, Dimitri N. Mavris, and Evangelos A. Theodorou. "Locally Adaptive Online Trajectory Optimization in Unknown Environments With RRTs." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-8997.

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Rapidly Exploring Random Trees (RRTs) have gained significant attention due to provable properties such as completeness and asymptotic optimality. However, offline methods are only useful when the entire problem landscape is known a priori. Furthermore, many real world applications have problem scopes that are orders of magnitude larger than typical mazes and bug traps that require large numbers of samples to match typical sample densities, resulting in high computational effort for reasonably low-cost trajectories. In this paper we propose an online trajectory optimization algorithm for uncertain large environments using RRTs, which we call Locally Adaptive Rapidly Exploring Random Tree (LARRT). This is achieved through two main contributions. We use an adaptive local sampling region and adaptive sampling scheme which depend on states of the dynamic system and observations of obstacles. We also propose a localized approach to planning and re-planning through fixing the root node to the current vehicle state and adding tree update functions. LARRT is designed to leverage local problem scope to reduce computational complexity and obtain a total lower-cost solution compared to a classical RRT of a similar number of nodes. Using this technique we can ensure that popular variants of RRT will remain online even for prohibitively large planning problems by transforming a large trajectory optimization approach to one that resembles receding horizon optimization. Finally, we demonstrate our approach in simulation and discuss various algorithmic trade-offs of the proposed approach.
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Xie, Bo, and Bin Yao. "New Approach of Tracking Control for a Class of Non-Minimum Phase Linear Systems." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42237.

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The paper presents a new tracking control approach for a class of non-minimum phase linear systems. The proposed approach consists of two parts: trajectory planning and tracking controller design. The trajectory planning is solved as an optimization problem to improve the achievable transient performance under the fundamental constraints associated with perfect tracking of non-minimum phase systems. The recently proposed adaptive robust tracking controller for a class of non-minimum phase systems is then applied to guarantee that the tracking error dynamics can be stabilized with bounded internal states. The effectiveness of the proposed approach is illustrated through simulation on tracking control of a second order non-minimum phase linear system. Further works are underway to extend the proposed control strategy and trajectory design to a class of non-minimum phase nonlinear systems.
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7

Trucios, Luis E., Mahdi Tavakoli, and Kim Adams. "Adaptive tracking control for task-based robot trajectory planning." In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9283035.

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8

Svensson, Lars, Monimoy Bujarbaruah, Nitin R. Kapania, and Martin Torngren. "Adaptive Trajectory Planning and optimization at Limits of Handling." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967679.

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9

Lopez, Israel, and Nesrin Sarigul-Klijn. "Integrated Structural Damage Assessment, Motion Planning, and Decision-Making for Distressed Aircraft Under Uncertainty." In ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2009. http://dx.doi.org/10.1115/smasis2009-1315.

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Aircraft navigation can be safely accomplished by properly addressing the following: decision-making, obstacle perception, aircraft state estimation, and aircraft control. To develop a monolithic navigational system is probably an impossible task; instead a hierarchical decomposition is presented, which breaks down the safe recovery and landing of distressed aircraft into sub-problems that maximize the probability that the overall objective is achieved. Navigational performance is often hinder by in-flight damage or failures, which often results in mission failure and an inability to guide the aircraft to a safe landing. Uncertainty is a very important concern in recovery of damaged aircraft since it can cause infeasibilities, false diagnosis and prognosis causing further performance degradation and mission failure. The damaged aircraft is simulated via a simplified kinematic model. The different sources and perspectives of uncertainties in the damage assessment process and post-failure trajectory planning are presented and classified. The decision-making process for an emergency motion planning and landing is developed via the Dempster-Shafer evidence theory. The objective of the trajectory planning is to arrive at a target position while maximizing the safety of the aircraft given uncertain conditions. Simulations are presented for an emergency motion planning and landing that takes into account aircraft dynamics, path complexity, distance to landing site, runway characteristics, and subjective human decision.
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10

Fridovich-Keil, David, Sylvia L. Herbert, Jaime F. Fisac, Sampada Deglurkar, and Claire J. Tomlin. "Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8460863.

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