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Motion Planning for Carlike Robots Using a Probabilistic Learning Approach

Petr Švestka, Mark H. Overmars
1997 The international journal of robotics research  
Also, we thank Anil Rao, Otfried Schwarzkopf and Mark de Berg for useful comments.  ...  Acknowledgments We thank Geert-Jan Giezeman for the implementation of many crucial`geometric' routines (some of which are contained in the Plageo library, see Gie93]) used both implementations.  ...  L is allowed to fail rather often, but should motion planning for car-like robots and probabilistic/heuristic motion planning techniques.  ... 
doi:10.1177/027836499701600201 fatcat:tfg6qfzu3fh2papoedz5rzq6ge

Planning Whole-body Humanoid Locomotion, Reaching, and Manipulation [chapter]

Eiichi Yoshida, Claudia Esteves, Oussama Kanoun, Mathieu Poirier, Anthony Mallet, Jean-Paul Laumond, Kazuhito Yokoi
2010 Motion Planning for Humanoid Robots  
The presented approach benefits from two cutting edges of recent advancement in robotics: powerful probabilistic geometric and kinematic motion planning and advanced dynamic motion control for humanoids  ...  In this article we address the planning problem of whole-body motions by humanoid robots.  ...  As described later in Section 2, we generally adopt a two-stage approach for whole-body motion planning.  ... 
doi:10.1007/978-1-84996-220-9_4 fatcat:4kpvis5dkvbwvbfdlh6ywqx7nu

Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments [article]

Èric Pairet, Juan David Hernández, Marc Carreras, Yvan Petillot, Morteza Lahijanian
2020 arXiv   pre-print
In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees.  ...  The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: (i) incrementally mapping the surroundings to build an uncertainty-aware representation of the  ...  Acknowledgements The authors are grateful to Michael Mistry and Paola Ardón for all support and helpful discussions about this work.  ... 
arXiv:2004.12317v2 fatcat:5shpev4m3bfwlflzmobkr4t3xu

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Anish Pandey
2017 International Robotics & Automation Journal  
Figure 1: General classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm used for mobile robot navigation.  ...  Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques.  ...  In [90] , the authors have designed a navigational approach for multiple mobile robots using a neuro-fuzzy controller.  ... 
doi:10.15406/iratj.2017.02.00023 fatcat:m6viumq36zf5zbfeexua475gjy

2021 Index IEEE Robotics and Automation Letters Vol. 6

2021 IEEE Robotics and Automation Letters  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, LRA April 2021 958-965 Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach.  ...  ., +, LRA April 2021 2854-2861 Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach.  ... 
doi:10.1109/lra.2021.3119726 fatcat:lsnerdofvveqhlv7xx7gati2xu

Planning multiple paths with evolutionary speciation

C. Hocaoglu, A.C. Sanderson
2001 IEEE Transactions on Evolutionary Computation  
The system can be applied for planning paths for mobile robots, assembly, and articulated manipulators.  ...  Generation of multiple alternative paths is an example of multimodal search and, in our previous work, a new approach to multimodal function optimization has been developed using a genetic algorithm (GA  ...  Fogel and the anonymous reviewers who made many useful suggestions.  ... 
doi:10.1109/4235.930309 fatcat:wkb65fdszrgw3fosbclrhakn6m

Soft Computing-Based Navigation Schemes for a Real Wheeled Robot Moving Among Static Obstacles

Nirmal Baran Hui, Dilip Kumar Pratihar
2007 Journal of Intelligent and Robotic Systems  
Two soft computingbased approaches, namely genetic-fuzzy system and genetic-neural system and a conventional potential field approach have been developed for this purpose.  ...  A CCD camera is used to collect information of the environment.  ...  [17] used a fuzzy logic controller (FLC) for planning collision-free motion of a car-like robot.  ... 
doi:10.1007/s10846-007-9190-5 fatcat:yrtyladcejbyrhweiw7h2pxoey

On responsiveness, safety, and completeness in real-time motion planning

Kris Hauser
2011 Autonomous Robots  
Replanning is a powerful mechanism for controlling robot motion under hard constraints and unpredictable disturbances, but it involves an inherent tradeoff between the planner's power (e.g., a planning  ...  It can also be applied in a contingency planning algorithm that achieves simultaneous safetyseeking and goal-seeking motion.  ...  Assisted Teleoperation Experiments on a 6DOF Manipulator Replanning interleaves planning and execution, so motion appears more fluid than a pre-planning approach.  ... 
doi:10.1007/s10514-011-9254-z fatcat:jmtzj6dplze3pgjn3ayvhtez4q

Realtime Informed Path Sampling for Motion Planning Search [chapter]

Ross A. Knepper, Matthew T. Mason
2016 Springer Tracts in Advanced Robotics  
We provide experimental results in simulation for motion planning on mobile robots, demonstrating up to a 330% increase in paths surviving collision test.  ...  Mobile robot motions often originate from an uninformed path-sampling process such as random or low-dispersion sampling.  ...  Here we report results for a class of carlike mobile robot.  ... 
doi:10.1007/978-3-319-29363-9_23 fatcat:6wsdaeft7jhirhisdctvcsgb4e

Long-Range Indoor Navigation with PRM-RL [article]

Anthony Francis and Aleksandra Faust and Hao-Tien Lewis Chiang and Jasmine Hsu and J. Chase Kew and Marek Fiser and Tsang-Wei Edward Lee
2020 arXiv   pre-print
Here we use Probabilistic Roadmaps (PRMs) as the sampling-based planner, and AutoRL as the reinforcement learning method in the indoor navigation context.  ...  We achieve this with PRM-RL, a hierarchical robot navigation method in which reinforcement learning agents that map noisy sensors to robot controls learn to solve short-range obstacle avoidance tasks,  ...  learning has recently gained popularity in solving motion planning problems for systems with unknown dynamics [41] , and has enabled robots to learn tasks that have been previously difficult or impossible  ... 
arXiv:1902.09458v2 fatcat:x5jqwb3gjrdshi4z6zb3elfgym

Custom Distribution for Sampling-Based Motion Planning [article]

Gabriel O. Flores-Aquino, J. Irving Vasquez-Gomez, O. Octavio Gutierrez-Frias
2022 arXiv   pre-print
Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces.  ...  For robots with large configuration spaces or dynamic restrictions, selecting these parameters is a challenging task.  ...  In particular, we are interested in motion planning for autonomous vehicles where the problem has been addressed from formal approaches like control theory [5] to machine learning.  ... 
arXiv:2104.10292v3 fatcat:32ulyiw5drbn7dpsmphmuuhdke

Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties [article]

Gokhan Alcan, Ville Kyrki
2022 arXiv   pre-print
In this paper, we propose Safe-CDDP, a safe trajectory optimization and control approach for systems under additive uncertainties and non-linear safety constraints based on constrained differential dynamic  ...  Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints.  ...  Since carlike robot requires non-holonomic motion to reach the goal position, longer prediction horizon (N = 120) is selected compared to point robot. c) 3D Quadrotor Robot: We lastly selected 3D quadrotor  ... 
arXiv:2011.01051v3 fatcat:tkdegdd4g5ftvnsuo2atrtvhf4

Balancing state-space coverage in planning with dynamics

Yanbo Li, Kostas E Bekris
2010 2010 IEEE International Conference on Robotics and Automation  
The premise of this paper is that it is possible to use statistical tools to learn quickly the effects of the constraints in the algorithm's state-space exploration during a training session.  ...  The paper provides proof of concept experiments comparing against and improving upon the standard RRT using MATLAB simulations for (a) swinging up different versions of a 3-link Acrobot system with dynamics  ...  The authors would also like to thank the anonymous reviewers for their helpful comments.  ... 
doi:10.1109/robot.2010.5509481 dblp:conf/icra/LiB10 fatcat:r73tdxk4gzg5rbdos6li6544aa

Dynamic and Safe Path Planning Based on Support Vector Machine among Multi Moving Obstacles for Autonomous Vehicles

Quoc Huy DO, Seiichi MITA, Hossein Tehrani Nik NEJAD, Long HAN
2013 IEICE transactions on information and systems  
We propose a practical local and global path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semistructured (or unstructured) environment, where obstacles are detected online  ...  His research field is path planning for autonomous vehicle.  ...  To handle moving obstacles, we proposed a probabilistic approach that uses particle filter. The vehicle's dynamic attributes are always considered for generating the local path.  ... 
doi:10.1587/transinf.e96.d.314 fatcat:a2cpowim6jb33h2gxxpatyypeu

Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties

Gokhan Alcan, Ville Kyrki
2022 IEEE Robotics and Automation Letters  
In this letter, we propose Safe-CDDP, a safe trajectory optimization and control approach for systems under additive uncertainties and nonlinear safety constraints based on constrained differential dynamic  ...  Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints.  ...  The authors are with the Intelligent Robotics Group, Department of Electrical Engineering and Automation (EEA), Aalto University, 02150 Espoo, Finland (e-mail: gokhan.alcan@aalto.fi; ville.kyrki@aalto.fi  ... 
doi:10.1109/lra.2022.3141192 fatcat:f57wchcelndahjcldoptfhbu3m
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