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

  • Authors:

  • Mac Schwager;Daniela Rus;Jean-Jacques Slotine

  • Affiliations:

  • GRASP Laboratory, University of Pennsylvania, 3330 WalnutSt, Philadelphia, PA 19106, USA;Computer Science and Artificial Intelligence Laboratory,MIT, 32 Vassar Street, Cambridge, MA 02139, USA;Nonlinear Systems Laboratory, MIT, 77 MassachusettsAvenue, Cambridge, MA 02139, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2011

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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.