The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a sampling-based path planning and replanning algorithm that produces anytime solutions. Our algorithm tunes the quality of its result based on available search time by generating a series of solutions, each guaranteed to be better than the previous ones by a user-defined improvement bound. When updated information regarding the underlying search space is received, the algorithm efficiently repairs its previous solution. The result is an approach that provides low-cost solutions to high-dimensional search problems involving partially-known or dynamic environments. We discuss theoretical properties of the algorithm, provide experimental results on a simulated multirobot planning scenario, and present an implementation on a team of outdoor mobile robots.

Notes

Text Reference

David Ferguson and Anthony (Tony) Stentz, "Anytime, Dynamic Planning in High-dimensional Search Spaces," IEEE International Conference on Robotics and Automation (ICRA), 2007.

BibTeX Reference

@inproceedings{Ferguson_2007_5945,
   author = "David Ferguson and Anthony (Tony) Stentz",
   title = "Anytime, Dynamic Planning in High-dimensional Search Spaces",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   year = "2007",
}

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