Anytime dynamic path-planning with flexible probabilistic roadmaps

Probabilistic roadmaps (PRM) have been demonstrated to be very promising for planning paths for robots with high degrees of freedom in complex 3D workspaces. In this paper we describe a PRM path-planning method presenting three novel features that are useful in various real-world applications. First, it handles zones in the robot workspace with different degrees of desirability. Given the random quality of paths that are calculated by traditional PRM approaches, this provides a mean to specify a sampling strategy that controls the search process to generate better paths by simply annotating regions in the free workspace with degrees of desirability. Second, our approach can efficiently re-compute paths in dynamic environments where obstacles and zones can change shape or move concurrently with the robot. Third, it can incrementally improve the quality of a generated path, so that a suboptimal solution is available when required for immediate action, but get improved as more planning time is affordable

[1]  Brian Peacock,et al.  International Space Station Robotic Systems Operations - a Human Factors Perspective , 2002 .

[2]  Mark S. Boddy,et al.  An Analysis of Time-Dependent Planning , 1988, AAAI.

[3]  Sebastian Thrun,et al.  ARA*: Anytime A* with Provable Bounds on Sub-Optimality , 2003, NIPS.

[4]  Jean-Claude Latombe,et al.  A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking , 2001, ISRR.

[5]  Jean-Claude Latombe,et al.  On the Probabilistic Foundations of Probabilistic Roadmap Planning , 2006, Int. J. Robotics Res..

[6]  Oliver Brock,et al.  Sampling-Based Motion Planning Using Predictive Models , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  Yu-Chi Chang,et al.  Finding Narrow Passages with Probabilistic Roadmaps: The Small-Step Retraction Method , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Mark H. Overmars,et al.  Motion planning in environments with danger zones , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[9]  Olivier Lavialle,et al.  Consideration of obstacle danger level in path planning using A* and Fast-Marching optimisation: comparative study , 2003, Signal Process..

[10]  Anthony Stentz,et al.  A Guide to Heuristic-based Path Planning , 2005 .

[11]  Gildardo Sánchez-Ante,et al.  Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

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

[13]  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).

[14]  Sven Koenig,et al.  Improved fast replanning for robot navigation in unknown terrain , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[15]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.

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