Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment
Name
Slotine_Unifying geometric.pdf
Size
566.24 KB
Format
Adobe PDF
Checksum (MD5)
4c8b5f0d25632f90b5511b7ee1e96347
Author(s)
Schwager, Mac
Rus, Daniela L.
Slotine, Jean-Jacques E
Date Issued
September 2010
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
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.
MIT 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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364910383444
