A scalable method for parallelizing sampling-based motion planning algorithms

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.

[1]  Lydia E. Kavraki,et al.  Distributed Sampling-Based Roadmap of Trees for Large-Scale Motion Planning , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[2]  Lydia Tapia,et al.  C-space Subdivision and Integration in Feature-Sensitive Motion Planning , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  Nancy M. Amato,et al.  Probabilistic roadmap methods are embarrassingly parallel , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[4]  Nancy M. Amato,et al.  Parallel protein folding with STAPL , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[5]  Nancy M. Amato,et al.  Using motion planning to study protein folding pathways , 2001, J. Comput. Biol..

[6]  Nancy M. Amato,et al.  Enhancing Randomized Motion Planners: Exploring with Haptic Hints , 2001, Auton. Robots.

[7]  Nancy M. Amato,et al.  The STAPL parallel container framework , 2011, PPoPP '11.

[8]  Nancy M. Amato,et al.  RESAMPL: A Region-Sensitive Adaptive Motion Planner , 2008, WAFR.

[9]  Emilio Frazzoli,et al.  Massively parallelizing the RRT and the RRT , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Enrico Pagello,et al.  On Parallel RRTs for Multi-robot Systems , 2002 .

[11]  Jean-Claude Latombe,et al.  Planning motions with intentions , 1994, SIGGRAPH.

[12]  Dinesh Manocha,et al.  A hybrid approach for complete motion planning , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Lydia Tapia,et al.  A Machine Learning Approach for Feature-Sensitive Motion Planning , 2004, WAFR.

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

[15]  Tsai-Yen Li,et al.  Assembly maintainability study with motion planning , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[16]  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).

[17]  Jean-Claude Latombe,et al.  A Motion Planning Approach to Flexible Ligand Binding , 1999, ISMB.

[18]  Rajeev Motwani,et al.  Path planning in expansive configuration spaces , 1997, Proceedings of International Conference on Robotics and Automation.

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

[20]  Lydia E. Kavraki,et al.  Capturing the Connectivity of High-Dimensional Geometric Spaces by Parallelizable Random Sampling Techniques , 1998, IPPS/SPDP Workshops.

[21]  Didier Devaurs,et al.  Parallelizing RRT on distributed-memory architectures , 2011, 2011 IEEE International Conference on Robotics and Automation.

[22]  Dominik Henrich,et al.  Fast Motion Planning by Parallel Processing – a Review , 1997, J. Intell. Robotic Syst..

[23]  Nancy M. Amato,et al.  Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch , 2000 .

[24]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[25]  Nancy M. Amato,et al.  STAPL: standard template adaptive parallel library , 2010, SYSTOR '10.

[26]  Rodney A. Brooks,et al.  A subdivision algorithm in configuration space for findpath with rotation , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

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

[28]  Kostas E. Bekris,et al.  Sampling-based roadmap of trees for parallel motion planning , 2005, IEEE Transactions on Robotics.