Coordination and Control of Autonomous Mobile Robot Systems with Entropy as a Dualistic Performance Measure

Intelligent control and coordination of an Au-tonomous Mobile Robot (AMR) fleet is a challenging problem. Optimization of the fleet performance using centralized control is feasible only for small fleets. Decentralized control, on the other hand, is feasible also for large fleets and features flexibility and robustness. We emphasize this flexibility and robustness and consider intelligent control as the capability of a system of mobile robots to manage and handle as many maintenance tasks as possible at any time. We focus on task assignments and use entropy in a dualistic way. For task assignments, maximization of potential entropy is sought for the set of tasks supported by available AMRs. By contrast, task performance is considered by minimization of the expected actual entropy of the AMR's task execution. Distributed fleet control is proposed with a contract negotiation protocol utilizing the entropy maximization and minimization measures.