Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain
@article{Yershova2005DynamicDomainRE,
title={Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain},
author={Anna Yershova and L{\'e}onard Jaillet and Thierry Sim{\'e}on and Steven M. LaValle},
journal={Proceedings of the 2005 IEEE International Conference on Robotics and Automation},
year={2005},
pages={3856-3861}
}Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing…
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References
SHOWING 1-10 OF 74 REFERENCES
A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking
- Computer ScienceISRR
- 2001
Experimental results show that this combination of techniques drastically reduces planning times, making it possible to handle difficult problems, including multi-robot problems in geometrically complex environments.
RRT-connect: An efficient approach to single-query path planning
- Computer ScienceProceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
- 2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two…
A framework for using the workspace medial axis in PRM planners
- Materials ScienceProceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
- 2000
This work examines the medial axis as a skeleton, including a method to compute an approximation to it, and forms a heuristic for finding difficult configurations using the medialaxis, and demonstrates its effectiveness in a planner for rigid objects in a 3D workspace.
The Gaussian sampling strategy for probabilistic roadmap planners
- Computer ScienceProceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
- 1999
This paper presents a new, simple sampling strategy, which it is called the Gaussian sampler, that gives a much better coverage of the difficult parts of the free configuration space.
MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space
- Computer ScienceProceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)
- 1999
A new method of sampling the configuration space in which randomly generated configurations are retracted onto the medial axis of the free space is proposed, and it is shown that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it.
Motion planning a aerial robot using rapidly-exploring random trees with dynamic constraints
- Engineering2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)
- 2003
A rapidly-exploring random tree (RRT) is used for the motion planning of a blimp system, and some techniques for improving the performance of the planner are proposed.
Incrementally reducing dispersion by increasing Voronoi bias in RRTs
- Computer ScienceIEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
- 2004
Initial results show that the new RRT-like planners based on exact Voronoi diagram computation are advantageous compared to existing RRTs, especially with respect to the number of collision checks and nodes in the search tree.
Kinodynamic motion planning: connecting exploration trees using trajectory optimization Methods
- Computer ScienceIEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004
- 2004
This paper proposes a solution to cope with the complexity issue of motion planning for complex dynamic systems for which no steering method is known by running a tree exploration method with a big neighborhood and locally modifying the trajectory to make it reach exactly the goal.
Manipulation Planning with Probabilistic Roadmaps
- Computer ScienceInt. J. Robotics Res.
- 2004
A general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches.
Path planning using lazy PRM
- Computer ScienceProceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
- 2000
The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner.