ICRA 2012 Paper Abstract

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Paper WeB07.1

Jacobs, Sam Ade (Texas A&M University), Manavi, Kasra (Texas A & M University), Burgos, Juan (Texas A & M University), Denny, Jory (Texas A&M University), Thomas, Shawna (Texas A&M University), Amato, Nancy (Texas A&M University)

A Scalable Method for Parallelizing Sampling-Based Motion Planning Algorithms

Scheduled for presentation during the Regular Session "Sampling-Based Motion Planning" (WeB07), Wednesday, May 16, 2012, 10:30−10:45, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on November 18, 2017

Keywords Motion and Path Planning, Parallel Robots

Abstract

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.

 

 

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