We study multi-robot caravanning, which is loosely deﬁned as the problem of a heterogeneous team of robots visiting speciﬁc areas of an environment (waypoints) as a group. After formally deﬁning this problem, we propose a novel solution that requires minimal communication and scales with the number of waypoints and robots. Our approach restricts explicit communication and coordination to occur only when robots reach waypoints, and relies on implicit coordination when moving between a given pair of waypoints. At the heart of our algorithm is the use of leader election to efﬁciently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment.
We implement our approach both in simulation and on a physical platform, and characterize the performance of the approach under various scenarios. We demonstrate that our approach can successfully be used to combine the planning capabilities of different agents.