Algorithms and complexity results for graph-based pursuit evasion

  • Authors:
  • Richard Borie;Craig Tovey;Sven Koenig

  • Affiliations:
  • Department of Computer Science, University of Alabama, Tuscaloosa, USA 35487;Department of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA 30332;Computer Science Department, University of Southern California, Los Angeles, USA 90089

  • Venue:
  • Autonomous Robots
  • Year:
  • 2011

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Abstract

We study the classical edge-searching pursuit-evasion problem where a number of pursuers have to clear a given graph of fast-moving evaders despite poor visibility, for example, where robots search a cave system to ensure that no terrorists are hiding in it. We study when polynomial-time algorithms exist to determine how many robots are needed to clear a given graph (minimum robot problem) and how a given number of robots should move on the graph to clear it with either a minimum sum of their travel distances (minimum distance problem) or minimum task-completion time (minimum time problem). The robots cannot clear a graph if the vertex connectivity of some part of the graph exceeds the number of robots. Researchers therefore focus on graphs whose subgraphs can always be cut at a limited number of vertices, that is, graphs of low treewidth, typically trees. We describe an optimal polynomial-time algorithm, called CLEARTHETREE, that is shorter and algorithmically simpler than the state-of-the-art algorithm for the minimum robot problem on unit-width unit-length trees. We then generalize prior research to both unit-width arbitrary-length and unit-length arbitrary-width graphs and derive both algorithms and time complexity results for a variety of graph topologies. Pursuit-evasion problems on the former graphs are generally simpler than pursuit-evasion problems on the latter graphs. For example, the minimum robot and distance problems are solvable in polynomial time on unit-width arbitrary-length trees but NP-hard on unit-length arbitrary-width trees.