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Published August 2011 | public
Journal Article

Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks

Abstract

A widely applied strategy for workload sharing is to equalize the workload assigned to each resource. In mobile multiagent systems, this principle directly leads to equitable partitioning policies whereby: 1) the environment is equitably divided into subregions of equal measure; 2) one agent is assigned to each subregion; and 3) each agent is responsible for service requests originating within its own subregion. The current lack of distributed algorithms for the computation of equitable partitions limits the applicability of equitable partitioning policies to limited-size multiagent systems operating in known, static environments. In this paper, first we design provably correct and spatially distributed algorithms that allow a team of agents to compute a convex and equitable partition of a convex environment. Second, we discuss how these algorithms can be extended so that a team of agents can compute, in a spatially distributed fashion, convex and equitable partitions with additional features, e.g., equitable and median Voronoi diagrams. Finally, we discuss two application domains for our algorithms, namely dynamic vehicle routing for mobile robotic networks and wireless ad hoc networks. Through these examples, we show how one can couple the algorithms presented in this paper with equitable partitioning policies to make these amenable to distributed implementation. More in general, we illustrate a systematic approach to devise spatially distributed control policies for a large variety of multiagent coordination problems. Our approach is related to the classic Lloyd algorithm and exploits the unique features of power diagrams.

Additional Information

© 2011 IEEE. Manuscript received October 05, 2010; revised December 06, 2010; accepted January 28, 2011. Date of publication February 07, 2011; date of current version August 03, 2011. This work was supported in part by the National Science Foundation under Grants #0705451 and #0705453 and the Office of Naval Research under Grant N00014-07-1-0721. Recommended by Associate Editor M. Egerstedt.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023