Towards High-Quality Battery Life for Autonomous Mobile Robot Fleets
Towards High-Quality Battery Life for Autonomous Mobile Robot Fleets
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DOI：
10.1109/acsos55765.2022.00024
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Akshar Shravan Chavan;Marco Brocanelli
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文献类型：
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作者：
Akshar Shravan Chavan;Marco Brocanelli
自主移动的机器人（AMR）依靠可充电电池在导航期间执行若干目标任务。先前的研究集中于通过跨多个AMR协调任务分配和/或充电调度来最小化任务停机时间。然而，它们并不能共同确保低任务停机时间和高质量的电池寿命。在本文中，我们提出了TCM，一个任务分配和充电管理器的AMR车队。TCM将目标任务分配给AMR，并安排其在可用充电站的充电时间，以最大限度地减少任务停机时间，并最大限度地提高AMR电池的寿命质量。我们制定的TCM问题作为一个MINLP问题，并提出了一个多项式时间的多周期TCM贪婪算法，定期适应其决策的高鲁棒性能量建模误差。我们的实验表明，相比，MINLP的实施在Guidelines求解器，所设计的算法提供了一个性能比为1.15的解决方案，在一小部分的执行时间。此外，与仅关注任务停机时间的代表性基线相比，TCM实现了类似的任务分配结果，同时提供了更高的电池寿命质量。
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.