Multi-robot caravanning is loosely deﬁned as the problem of a heterogeneous team of robots visiting speciﬁc areas of an environment (waypoints) as a group. 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. The below video shows an example of our system working in an office building.
We first analyzed our technique by determining how roadmap size affects the success rate of caravanning and leader switching. As seen by the figure on the right, as the size of the leader roadmap increased the success rate also increased.
We were also concerned with how the structure of the roadmap affected caravanning success rates. Specifically, if the roadmap ventured closer to the walls of the environment, caravanning should fail more often, whereas when the roadmap is farther from the walls, caravanning should succeed more. The plot on the left shows this affect. MAPRM, which has nodes farthest from the environment walls, has the highest success rate, whereas Gauss and OBPRM, which build maps close to walls, have the lowest success rates.
Multi-Robot Caravanning, Jory Denny, Andrew Giese, Aditya Mahadevan, Arnaud Marfaing, Rachel Glockenmeier, Colton Revia, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel.
Rob. Syst. (IROS), pp. 5722 - 5729, Tokyo, Japan, Nov 2013.
Supported by NSF