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Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment

Author(s)
Schwager, Mac; Rus, Daniela L.; Slotine, Jean-Jacques E
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Abstract
This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field-based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi-based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to non-convex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus.
Date issued
2010-09
URI
http://hdl.handle.net/1721.1/79093
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Nonlinear Systems Laboratory
Journal
The International Journal of Robotics Research
Publisher
Sage Publications
Citation
Schwager, Mac, Daniela L. Rus, and Jean-Jacques E. Slotine. “Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Deployment.” The International Journal of Robotics Research 30.3 (2010): 371–383.
Version: Author's final manuscript
ISSN
0278-3649
1741-3176

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