Robot Formations: Learning Minimum-Length Paths on Uneven Terrain

Abstract

We discuss a prototypeproblem involving terrain exploration and learning by formations ofautonomous vehicles. We investigate an algorithm forcoordinating multiple robots whose task is to find the shortest pathbetween a fixed pair of start and target locations, without access toa "global" map containing those locations.Odometry information alone isnot sufficient for minimizing path length if the terrain is uneven orif it includes obstacles. We generalize existing results on a simplecontrol law, also known as "local pursuit," which is appropriate inthe context of formations and which requires limited interactionbetween vehicles. Our algorithm is iterative and converges to alocally optimal path. We include simulations and experimentsillustrating the performance of the proposed strategy.The research and scientific content in this material has been published in the IEEE Mediterranean Conference on Control and Automation, July 2000.