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Carnegie Mellon Robotics Institute

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Abstract

We present a proof for the probabilistic completeness of RRT-based algorithms when planning with constraints on end-effector pose. Pose constraints can induce lower-dimensional constraint manifolds in the configuration space of the robot, making rejection sampling techniques infeasible. RRT-based algorithms can overcome this problem by using the sample-project method: sampling coupled with a projection operator to move configuration space samples onto the constraint manifold. Until now it was not known whether the sample-project method produces adequate coverage of the constraint manifold to guarantee probabilistic completeness. The proof presented in this paper guarantees probabilistic completeness for a class of RRT-based algorithms given an appropriate projection operator. This proof is valid for constraint manifolds of any fixed dimensionality.

Keywords

motion planning, constrained manipulation planning, probabilistic completeness, RRT

Notes

Sponsor: Intel Labs Pittsburgh, NSF,

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Personal Robotics

Number of pages: 7

Text Reference

Dmitry Berenson and Siddhartha Srinivasa, "Probabilistically Complete Planning with End-Effector Pose Constraints," IEEE International Conference on Robotics and Automation (ICRA '10), May, 2010.

BibTeX Reference

@inproceedings{Berenson_2010_6558,
   author = "Dmitry Berenson and Siddhartha Srinivasa",
   title = "Probabilistically Complete Planning with End-Effector Pose Constraints",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA '10)",
   month = "May",
   year = "2010",
}

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