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....

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Bibliographic Details
Published in:2012 IEEE International Conference on Robotics and Automation pp. 2529 - 2536
Main Authors: Jacobs, S. A., Manavi, K., Burgos, J., Denny, J., Thomas, S., Amato, N. M.
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Joining processes
Libraries
Planning
Probabilistic logic
Robots
Scalability
ISBN:9781467314039, 146731403X
ISSN:1050-4729
Online Access:Get full text
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Summary: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.
ISBN:9781467314039
146731403X
ISSN:1050-4729
DOI:10.1109/ICRA.2012.6225334