优化机器人任务规划以延长电池寿命
Robust Optimal Task Planning to Maximize Battery Life
- 用麦考密克包络法线性化双线性项，提升求解效率
- 在保证任务完成的前提下，使电池剩余电量最低保持在15%以上
- 适合需要长时间运行的工业级移动机器人场景
本文提出一种面向自主移动机器人（AMRs）的控制优化平台，旨在延长电池寿命的同时确保任务完成。快速任务规划与维持最低电池荷电状态的要求导致一个双线性优化问题。本文采用麦考密克包络技术对双线性项进行线性化处理，并设计了一种带松弛约束的新规划算法，可在参数不确定性下鲁棒高效地求解。仿真结果表明，所提方法能在满足任务完成要求的前提下，有效降低电池退化程度。
原文摘要 · Abstract (English)
This paper proposes a control-oriented optimization platform for autonomous mobile robots (AMRs), focusing on extending battery life while ensuring task completion. The requirement of fast AMR task planning while maintaining minimum battery state of charge, thus maximizing the battery life, renders a bilinear optimization problem. McCormick envelop technique is proposed to linearize the bilinear term. A novel planning algorithm with relaxed constraints is also developed to handle parameter uncertainties robustly with high efficiency ensured. Simulation results are provided to demonstrate the utility of the proposed methods in reducing battery degradation while satisfying task completion requirements.
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