用模型预测控制优化无人机配送路径与成本。
Optimal Path Planning and Cost Minimization for a Drone Delivery System Via Model Predictive Control
- 将无人机配送转为控制问题，用模型预测控制求解。
- 相比三种强化学习方法，MPC更快达成最优路径且需更少无人机。
- 适合关注高效配送系统设计的研究者和工程实践者。
本研究将无人机配送问题建模为控制问题，并采用模型预测控制（MPC）求解。实验分为两个场景：第一个在低维、较简单的网格世界环境中进行；第二个则具有更高维度和更强复杂性。将MPC方法与三种主流多智能体强化学习算法（独立Q学习、联合动作学习、值分解网络）进行对比。结果表明，MPC方法在更短时间内完成任务，所需最优无人机数量更少，同时实现更低的配送成本并规划出最优路径。
原文摘要 · Abstract (English)
In this study, we formulate the drone delivery problem as a control problem and solve it using Model Predictive Control. Two experiments are performed: The first is on a less challenging grid world environment with lower dimensionality, and the second is with a higher dimensionality and added complexity. The MPC method was benchmarked against three popular Multi-Agent Reinforcement Learning (MARL): Independent $Q$-Learning (IQL), Joint Action Learners (JAL), and Value-Decomposition Networks (VDN). It was shown that the MPC method solved the problem quicker and required fewer optimal numbers of drones to achieve a minimized cost and navigate the optimal path.
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