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.<p>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. <p>Our algorithm is iterative and converges to alocally optimal path. We include simulations and experimentsillustrating the performance of the proposed strategy.<P><Center><I>The research and scientific content in this material has been published in the IEEE Mediterranean Conference on Control and Automation, July 2000.</I></Center>