# Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations

Published in Proceedings of the Robotics: Science and Systems Conference , 2017

Recommended citation: Siddharth Mayya, Pietro Pierpaoli, Girish Nair, and Magnus Egerstedt. Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations. In Proceedings of Robotics: Science and Systems, Cambridge, Massachusetts, July 2017. doi:10.15607/RSS.2017.XIII.044. http://www.roboticsproceedings.org/rss13/p44.pdf

As the spatial scale of robots decrease in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots’ surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multi-robot experiments illustrate the feasibility of the proposed approach, and demonstrate how collisions in multi-robot systems can indeed be employed as useful information sources.