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Dynamic Assignment in Distributed Motion Planning With Local Coordination
Departmental Papers (ESE)
  • Michael Zavlanos, University of Pennsylvania
  • George J Pappas, University of Pennsylvania
Distributed motion planning of multiple agents raises fundamental and novel problems in control theory and robotics. In particular, in applications such as coverage by mobile sensor networks or multiple target tracking, a great new challenge is the development of motion planning algorithms that dynamically assign targets or destinations to multiple homogeneous agents, not relying on any a priori assignment of agents to destinations. In this paper, we address this challenge using two novel ideas. First, distributed multi-destination potential fields are developed that are able to drive every agent to any available destination. Second, nearest neighbor coordination protocols are developed ensuring that distinct agents are assigned to distinct destinations. Integration of the overall system results in a distributed, multiagent, hybrid system for which we show that the mutual exclusion property of the final assignment is guaranteed for almost all initial conditions. Furthermore, we show that our dynamic assignment algorithm will converge after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of purely discrete assignment problems. Our scalable approach is illustrated with nontrivial computer simulations.
Document Type
Journal Article
Date of this Version
Copyright 2008 IEEE. Reprinted from IEEE Transactions on Robotics, Volume 24, Issue 1, February 2008, pages 232-242.

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  • distributed control,
  • hybrid systems,
  • motion planning,
  • multiagent assignment problems
Citation Information
Michael Zavlanos and George J Pappas. "Dynamic Assignment in Distributed Motion Planning With Local Coordination" (2008)
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