Robot formations: Learning minimum-length paths on uneven terrain

Abstract

We discuss a prototype problem involving terrain exploration and learning by formations of autonomous vehicles. We investigate an algorithm for coordinating multiple robots whose task is to nd the shortest path between a xed pair of start and target locations, without access to a global map containing those locations. Odometry information alone is not sucient for minimizing path length if the terrain is uneven or if it includes obstacles. We generalize existing results on a simple control law, also known as \local pursuit, which is appropriate in the context of formations and which requires limited interaction between vehicles. Our algorithm is iterative and converges to a locally optimal path. We include simulations and experiments illustrating the performance of the proposed strategy.