Center for Human Modeling and Simulation

Document Type

Conference Paper

Date of this Version

2011

Publication Source

Proceedings, The Fourth International Symposium on Combinatorial Search (SoCS-2011)

Abstract

Path planning quickly becomes computationally hard as the dimensionality of the state-space increases. In this paper, we present a planning algorithm intended to speed up path planning for high-dimensional state-spaces such as robotic arms. The idea behind this work is that while planning in a highdimensional state-space is often necessary to ensure the feasibility of the resulting path, large portions of the path have a lower-dimensional structure. Based on this observation, our algorithm iteratively constructs a state-space of an adaptive dimensionality–a state-space that is high-dimensional only where the higher dimensionality is absolutely necessary for finding a feasible path. This often reduces drastically the size of the state-space, and as a result, the planning time and memory requirements. Analytically, we show that our method is complete and is guaranteed to find a solution if one exists, within a specified suboptimality bound. Experimentally, we apply the approach to 3D vehicle navigation (x, y, heading), and to a 7 DOF robotic arm on the Willow Garage’s PR2 robot. The results from our experiments suggest that our method can be substantially faster than some of the state-ofthe-art planning algorithms optimized for those tasks.

Copyright/Permission Statement

Copyright © 2011, Association for the Advancement of Artificial Intelligence. Available at http://www.aaai.org/ocs/index.php/SOCS/SOCS11/paper/view/4037

Keywords

motion and path planning, planning algorithms, heuristic search

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Date Posted: 13 January 2016