用模型预测控制解决带权重的覆盖路径规划问题，提升搜救效率。
On the Application of Model Predictive Control to a Weighted Coverage Path Planning Problem
- 采用带覆盖约束的模型预测控制新方法
- 初始化为TSP启发式后性能显著提升
- 适合连续空间下的智能体路径规划任务
本文研究将模型预测控制（MPC）应用于加权覆盖路径规划（WCPP）问题。该问题广泛存在于搜救（SAR）等实际场景中：一个或多个智能体在给定搜索空间内移动，并从空间分布的奖励点获取收益。与人工势场不同，每个奖励点只能被收集一次；与旅行商问题（TSP）不同，智能体在连续空间中移动，且无需覆盖所有位置或可返回已访问区域。本文提出一种新的带覆盖约束（CCs）的MPC公式，实验表明，若使用基于TSP的启发式进行初始化，求解效果更优。在小规模仿真中，该方法明显优于基础的MPC方案。
原文摘要 · Abstract (English)
This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, including search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and collect rewards from a given spatial distribution. Unlike an artificial potential field, each reward can only be collected once. In contrast to a Traveling Salesman Problem (TSP), the agent moves in a continuous space. Moreover, he is not obliged to cover all locations and/or may return to previously visited locations. The WCPP problem is tackled by a new Model Predictive Control (MPC) formulation with so-called Coverage Constraints (CCs). It is shown that the solution becomes more effective if the solver is initialized with a TSP-based heuristic. With and without this initialization, the proposed MPC approach clearly outperforms a naive MPC formulation, as demonstrated in a small simulation study.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。