2005
DOI: 10.1109/tro.2005.847599
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Sampling-based roadmap of trees for parallel motion planning

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Cited by 136 publications
(83 citation statements)
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References 37 publications
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“…It is greatly motivated by the availability of new robotic systems and the need to produce paths that respect the physical constraints in the motion of the robotic systems and hence can be translated into trajectories executed by the real platforms with minimum effort. Sampling-based tree planners, such as Rapidly-exploring Random Tree (RRT) [6], Expansive Space Tree (EST) [7], and others [1]- [5], [8]- [10] have in recent years been widely successful in kinodynamic motion planning. Such planners typically explore the state space using a single or a bidirectional tree [1]- [5], [7], [9], or multiple trees, as in the case of Sampling-based Roadmap of Trees (SRT) [10].…”
Section: Introductionmentioning
confidence: 99%
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“…It is greatly motivated by the availability of new robotic systems and the need to produce paths that respect the physical constraints in the motion of the robotic systems and hence can be translated into trajectories executed by the real platforms with minimum effort. Sampling-based tree planners, such as Rapidly-exploring Random Tree (RRT) [6], Expansive Space Tree (EST) [7], and others [1]- [5], [8]- [10] have in recent years been widely successful in kinodynamic motion planning. Such planners typically explore the state space using a single or a bidirectional tree [1]- [5], [7], [9], or multiple trees, as in the case of Sampling-based Roadmap of Trees (SRT) [10].…”
Section: Introductionmentioning
confidence: 99%
“…Sampling-based tree planners, such as Rapidly-exploring Random Tree (RRT) [6], Expansive Space Tree (EST) [7], and others [1]- [5], [8]- [10] have in recent years been widely successful in kinodynamic motion planning. Such planners typically explore the state space using a single or a bidirectional tree [1]- [5], [7], [9], or multiple trees, as in the case of Sampling-based Roadmap of Trees (SRT) [10]. Recent books [1], [2] contain additional references and descriptions of many successful sampling-based tree planners.…”
Section: Introductionmentioning
confidence: 99%
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“…This method is not adapted for dynamic environments since a change in the environment causes the reconstruction of the whole graph. Several variants of these methods were proposed: Visibility based PRM [4], Medial axis PRM [5], Lazy PRM [6] and sampling based roadmap of trees [7]. Other methods are used and Helguera et all used a local method to plan paths for manipulator robots and solved the local minima problem by making a search in a graph describing the local environment using and A* algorithm until the local minima are avoided [8].…”
Section: Related Workmentioning
confidence: 99%
“…At this point we generally have a forest of trees of configurations. Finally, we attempt to connect the trees using an RRT algorithm as in the sampling-based roadmap of trees [19].…”
Section: Conditional Reachability Graphmentioning
confidence: 99%