Distributed on-line dynamic task assignment for multi-robot patrolling

Multi-robot patrolling is a key feature for various applications related to surveillance and security, and it has been studied from several different perspectives, ranging from techniques that devise optimal off-line strategies to implemented systems. However, still few approaches consider on-line decision techniques that can cope with uncertainty and non-determinism in robot behaviors. In this article we address on-line coordination, by casting the multi-robot patrolling problem as a task assignment problem and proposing two solution techniques: DTA-Greedy, which is a baseline greedy approach, and DTAP, which is based on sequential single-item auctions. We evaluate the performance of our system in a realistic simulation environment (built with ROS and stage) as well as on real robotic platforms. In particular, in the simulated environment we compare our task assignment approaches with previous off-line and on-line methods. Our results confirm that on-line coordination approaches improve the performance of the multi-robot patrolling system in real environments, and that coordination approaches that employ more informed coordination protocols (e.g., DTAP) achieve better performances with respect to state-of-the-art online approaches (e.g., SEBS) in scenarios where interferences among robots are likely to occur. Moreover, the deployment on real platforms (three Turtlebots in an office environment) shows that our on-line approaches can successfully coordinate the robots achieving good patrolling behaviors when facing typical uncertainty and noise (e.g., localization and navigation errors) associated to real platforms.