Efficient and inefficient ant coverage methods

  • Authors:

  • Sven Koenig;Boleslaw Szymanski;Yaxin Liu

  • Affiliations:

  • College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332-0280, USA E-mail: skoenig@cc.gatech.edu;Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180-3590, USA E-mail: szymansk@cs.rpi.edu;College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332-0280, USA E-mail: yxliu@cc.gatech.edu

  • Venue:
  • Annals of Mathematics and Artificial Intelligence
  • Year:
  • 2001

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Abstract

Ant robots are simple creatures with limited sensing and computational capabilities. They have the advantage that they are easy to program and cheap to build. This makes it feasible to deploy groups of ant robots and take advantage of the resulting fault tolerance and parallelism. We study, both theoretically and in simulation, the behavior of ant robots for one-time or repeated coverage of terrain, as required for lawn mowing, mine sweeping, and surveillance. Ant robots cannot use conventional planning methods due to their limited sensing and computational capabilities. To overcome these limitations, we study navigation methods that are based on real-time (heuristic) search and leave markings in the terrain, similar to what real ants do. These markings can be sensed by all ant robots and allow them to cover terrain even if they do not communicate with each other except via the markings, do not have any kind of memory, do not know the terrain, cannot maintain maps of the terrain, nor plan complete paths. The ant robots do not even need to be localized, which completely eliminates solving difficult and time-consuming localization problems. We study two simple real-time search methods that differ only in how the markings are updated. We show experimentally that both real-time search methods robustly cover terrain even if the ant robots are moved without realizing this (say, by people running into them), some ant robots fail, and some markings get destroyed. Both real-time search methods are algorithmically similar, and our experimental results indicate that their cover time is similar in some terrains. Our analysis is therefore surprising. We show that the cover time of ant robots that use one of the real-time search methods is guaranteed to be polynomial in the number of locations, whereas the cover time of ant robots that use the other real-time search method can be exponential in (the square root of) the number of locations even in simple terrains that correspond to (planar) undirected trees.