Results 221 to 230 of about 43,065 (262)
Proceedings of the International Symposium on Combinatorial Search, 2022 Sampling-based motion planning algorithms such as Rapidly exploring Random Trees (RRTs) have been used in robotic applications for a long time. In this paper, we propose a method that combines deep learning with RRT* method. We use a neural network to learn a sample strategy for RRT*.We evaluate Deep RRT* in a collection of 2D scenarios.
Xuzhe Dang +2 more
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SR-RRT: Selective retraction-based RRT planner
2012 IEEE International Conference on Robotics and Automation, 2012 We present a novel retraction-based planner, selective retraction-based RRT, for efficiently handling a wide variety of environments that have different characteristics. We first present a bridge line-test that can identify regions around narrow passages, and then perform an optimization-based retraction operation selectively only at those regions.
Junghwan Lee +3 more
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Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006
We present a replanning algorithm for repairing rapidly-exploring random trees when changes are made to the configuration space. Instead of abandoning the current RRT, our algorithm efficiently removes just the newly-invalid parts and maintains the rest. It then grows the resulting tree until a new solution is found.
Dave Ferguson 0001 +2 more
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We present a replanning algorithm for repairing rapidly-exploring random trees when changes are made to the configuration space. Instead of abandoning the current RRT, our algorithm efficiently removes just the newly-invalid parts and maintains the rest. It then grows the resulting tree until a new solution is found.
Dave Ferguson 0001 +2 more
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FC-RRT*: A modified RRT* with rapid convergence in complex environments
Journal of Computational ScienceYankui Song, Yaoyao Tuo, Chengguo Liu
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RRT-blossom: RRT with a local flood-fill behavior
Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006This paper proposes a new variation of the RRT planner which demonstrates good performance on both loosely-constrained and highly-constrained environments. The key to the planner is an implicit flood-fill-like mechanism, a technique that is well suited to escaping local minima in highly constrained problems.
Maciej Kalisiak, Michiel van de Panne
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MK-RRT*: Multi-Robot Kinodynamic RRT Trajectory Planning
2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021This paper introduces MK-RRT*: a Multi-robot Kinodynamic RRT*-based framework for trajectory planning of multiple dynamically-modeled robots. The framework includes both tightly-coupled and loosely-coupled methods for planning. The simultaneous, tightly-coupled, method provides an asymptotically optimal solution to the multi-robot trajectory planning ...
Brennan Cain +2 more
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2015 IEEE International Conference on Robotics and Automation (ICRA), 2015
Reachable volumes are a new technique that allows one to efficiently restrict sampling to feasible/reachable regions of the planning space even for high degree of freedom and highly constrained problems. However, they have so far only been applied to graph-based sampling-based planners.
Troy McMahon +2 more
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Reachable volumes are a new technique that allows one to efficiently restrict sampling to feasible/reachable regions of the planning space even for high degree of freedom and highly constrained problems. However, they have so far only been applied to graph-based sampling-based planners.
Troy McMahon +2 more
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Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games, 2015
This paper presents a novel algorithm for real-time path-planning in a dynamic environment such as a computer game. We utilize a real-time sampling approach based on the Rapidly Exploring Random Tree (RRT) algorithm that has enjoyed wide success in robotics. More specifically, our algorithm is based on the RRT* and informed RRT* variants. We contribute
Rajamäki, Joose +3 more
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This paper presents a novel algorithm for real-time path-planning in a dynamic environment such as a computer game. We utilize a real-time sampling approach based on the Rapidly Exploring Random Tree (RRT) algorithm that has enjoyed wide success in robotics. More specifically, our algorithm is based on the RRT* and informed RRT* variants. We contribute
Rajamäki, Joose +3 more
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Steps toward derandomizing RRTs
Proceedings of the Fourth International Workshop on Robot Motion and Control (IEEE Cat. No.04EX891), 2004We present two new motion planning algorithms, based on the rapidly exploring random tree (RRT) family of algorithms. These algorithms represent the first work in the direction of derandomizing RRTs; this is a very challenging problem due to the way randomization is used in RRTs.
Stephen R. Lindemann, Steven M. LaValle
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