Multipartite RRTs for Rapid Replanning in Dynamic Environments

The rapidly-exploring random tree (RRT) algorithm has found widespread use in the field of robot motion planning because it provides a single-shot, probabilistically complete planning method which generalizes well to a variety of problem domains. We present the multipartite RRT (MP-RRT), an RRT variant which supports planning in unknown or dynamic environments. By purposefully biasing the sampling distribution and re-using branches from previous planning iterations, MP-RRT combines the strengths of existing adaptations of RRT for dynamic motion planning. Experimental results show MP-RRT to be very effective for planning in dynamic environments with unknown moving obstacles, replanning in high-dimensional configuration spaces, and replanning for systems with space time constraints.

[1]  Sean Quinlan.  Real-time modification of collision-free paths , 1994 .

[2]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[3]  Vladimir J. Lumelsky,et al.  Incorporating body dynamics into the sensor-based motion planning paradigm. The maximum turn strategy , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[4]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[5]  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).

[6]  E. Feron,et al.  Robust hybrid control for autonomous vehicle motion planning , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[7]  Scott Alan Hutchinson,et al.  Toward real-time path planning in changing environments , 2000 .

[8]  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).

[9]  Oliver Brock,et al.  Decomposition-based motion planning: a framework for real-time motion planning in high-dimensional configuration spaces , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[10]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[11]  Manuela M. Veloso,et al.  Real-time randomized path planning for robot navigation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Tsai-Yen Li,et al.  An incremental learning approach to motion planning with roadmap management , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[13]  Kostas E. Bekris,et al.  Probabilistic Roadmaps of Trees for Parallel Computation of Multiple Query Roadmaps , 2003, ISRR.

[14]  Morten Strandberg,et al.  Robot path planning : an object-oriented approach , 2004 .

[15]  Maja J. Mataric,et al.  Motion planning using dynamic roadmaps , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  Jing Xiao,et al.  Real-time adaptive and trajectory-optimized manipulator motion planning , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[17]  Yutaka Hirano,et al.  Image-based object recognition and dexterous hand/arm motion planning using RRTs for grasping in cluttered scene , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Jur P. van den Berg,et al.  Anytime path planning and replanning in dynamic environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[19]  Nidhi Kalra,et al.  Replanning with RRTs , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[20]  G. Swaminathan.  Robot Motion Planning , 2006 .