An efficient deterministic sequence for sampling-based motion planners

This paper presents a deterministic sequence that has the following good and useful features for sampling-based motion planners. It generates samples over a hierarchical grid structure in an incremental low-dispersion manner, allowing a uniform coverage of the configuration space. Due to its grid structure, neighborhood relationship between samples is easily computed, thus allowing roadmap-based planners to faster construct the roadmap. The sequence is computationally efficient and permits to locally control the degree of resolution required at each region of the configuration space by allowing the generation of more samples where they are most needed. The proposed sequence has been incorporated to a PRM-like planner and tested on a bend-corridor problem for different degrees of freedom

[1]  Danica Kragic,et al.  Artificial potential biased probabilistic roadmap method , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Steven M. LaValle,et al.  Quasi-randomized path planning , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[3]  Steven M. LaValle,et al.  Incremental Grid Sampling Strategies in Robotics , 2004, WAFR.

[4]  Steven M. LaValle,et al.  On the Relationship between Classical Grid Search and Probabilistic Roadmaps , 2004, Int. J. Robotics Res..

[5]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[6]  Lydia E. Kavraki,et al.  Path planning using lazy PRM , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[7]  Jean-Claude Latombe,et al.  On Delaying Collision Checking in PRM Planning: Application to Multi-Robot Coordination , 2002, Int. J. Robotics Res..

[8]  Mark H. Overmars,et al.  The Gaussian sampling strategy for probabilistic roadmap planners , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[9]  Jan Rosell.  Sampling SE(3) with a Deterministic Sequence for 3D Rigid-Body Path Planning , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Irene Gargantini,et al.  An effective way to represent quadtrees , 1982, CACM.

[11]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[12]  Nancy M. Amato,et al.  MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[13]  Jan Rosell,et al.  Path planning using Harmonic Functions and Probabilistic Cell Decomposition , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.