Entropy-based coordination for maintenance tasks of an autonomous mobile robot system

Maintenance tasks represent a potential area for applying multi-purpose Autonomous Mobile Robots (AMRs). Intelligent control and coordination of such a system is challenging and optimization methods are feasible only for small fleets. Decentralized control can provide flexibility and robustness, which are better applicable also for large fleets, though with less guaranteed performance. Our focus is on flexibility and robustness in task scheduling and task assignments and we use entropy as an indirect performance criterion for coordination, both at the system level (maximize entropy) and at the AMR level (minimize entropy). As a distributed coordination scheme, we use a modified contract negotiation protocol. We show preliminarily the feasibility of our approach with simulation results.