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Autonomous mobile robots (AMRs) are capable of carrying out operations continuously for 24/7, which enables them to optimize tasks, increase throughput, and meet demanding operational requirements. To ensure seamless and uninterrupted operations, an effective coordination of task allocation and charging schedules is crucial while considering the preservation of battery sustainability. Moreover, regular preventive main- tenance plays an important role in enhancing the robustness of AMRs against hardware failures and abnormalities during task execution. However, existing works do not consider the influence of properly scheduling AMR maintenance on both task downtime and battery lifespan. In this paper, we propose MTC, a maintenance-aware task and charging scheduler designed for fleets of AMR operating continuously in highly automated envi- ronments. MTC leverages Linear Programming (LP) to first help decide the best time to schedule maintenance for a given set of AMRs. Subsequently, the Kuhn-Munkres algorithm, a variant of the Hungarian algorithm, is used to finalize task assignments and carry out the charge scheduling to minimize the combined cost of task downtime and battery degradation. Experimental results demonstrate the effectiveness of MTC, reducing the combined total cost up to 3.45 times and providing up to 68% improvement in battery capacity degradation compared to the baselines. 

Autonomous Mobile Robots (AMRs) rely on rechargeable batteries to execute several objective tasks during navigation. Previous research has focused on minimizing task downtime by coordinating task allocation and/or charge scheduling across multiple AMRs. However, they do not jointly ensure low task downtime and high-quality battery life.In this paper, we present TCM, a Task allocation and Charging Manager for AMR fleets. TCM allocates objective tasks to AMRs and schedules their charging times at the available charging stations for minimized task downtime and maximized AMR batteries’ quality of life. We formulate the TCM problem as an MINLP problem and propose a polynomial-time multi-period TCM greedy algorithm that periodically adapts its decisions for high robustness to energy modeling errors. We experimentally show that, compared to the MINLP implementation in Gurobi solver, the designed algorithm provides solutions with a performance ratio of 1.15 at a fraction of the execution time. Furthermore, compared to representative baselines that only focus on task downtime, TCM achieves similar task allocation results while providing much higher battery quality of life.