/ODrM* optimal multirobot path planning in low dimensional search spaces

ODrM* optimal multirobot path planning in low dimensional search spaces

Cornelia Ferner, Glenn Wagner and Howie Choset
Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), pp. 3854 - 3859, May, 2013

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Abstract

We believe the core of handling the complexity of coordinated multiagent search lies in identifying which subsets of robots can be safely decoupled, and hence planned for in a lower dimensional space. Our work, as well as those of others take that perspective. In our prior work, we introduced an approach called subdimensional expansion for constructing low-dimensional but sufficient search spaces for multirobot path planning, and an implementation for graph search called M*. Subdimensional expansion dynamically increases the dimensionality of the search space in regions featuring significant robot-robot interactions. In this paper, we integrate M* with Meta-Agent Constraint-Based Search (MA-CBS), a planning framework that seeks to couple repeatedly colliding robots allowing for other robots to be planned in low-dimensional search space. M* is also integrated with operator decomposition (OD), an A*-variant performing lazy search of the outneighbors of a given vertex. We show that the combined algorithm demonstrates state of the art performance.

Notes
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6631119

BibTeX Reference
@conference{Ferner-2013-7730,
author = {Cornelia Ferner and Glenn Wagner and Howie Choset},
title = {ODrM* optimal multirobot path planning in low dimensional search spaces},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2013},
month = {May},
pages = {3854 - 3859},
publisher = {IEEE},
keywords = {multirobot path planning, optimal path planning},
}
2017-09-13T10:39:21+00:00