In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a moving target within a given deadline. We prove this problem to be NP-hard, and present the first set of Mixed-Integer Linear Programming (MILP) models to tackle the MESPP problem. Our models are the first to encompass multiple searchers, arbitrary capture ranges, and false negatives simultaneously. While state-of-the-art algorithms for MESPP are based on simple path enumeration, the adoption of MILP as a planning paradigm allows to leverage the powerful techniques of modern solvers, yielding better computational performance and, as a consequence, longer planning horizons. The models are designed for computing optimal solutions offline, but can be easily adapted for a distributed online approach. Our simulations show that it is possible to achieve 98% decrease in computational time relative to the previous state-of-the-art. We also show that the distributed approach performs nearly as well as the centralized, within 6% in the settings studied in this letter, with the advantage of requiring significant less time - an important consideration in practical search missions.

中文翻译：

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多机器人非对抗性搜索的混合整数线性规划模型


在这封信中，我们考虑了多机器人高效搜索路径规划（MESPP）问题，其中一组机器人被部署在图形表示的环境中，以在给定的期限内捕获移动目标。我们证明这个问题是 NP 困难的，并提出了第一组混合整数线性规划 (MILP) 模型来解决 MESPP 问题。我们的模型是第一个同时包含多个搜索者、任意捕获范围和漏报的模型。虽然最先进的 MESPP 算法基于简单的路径枚举，但采用 MILP 作为规划范例可以利用现代求解器的强大技术，从而产生更好的计算性能，从而获得更长的规划范围。这些模型专为离线计算最佳解决方案而设计，但可以轻松适应分布式在线方法。我们的模拟表明，与之前最先进的技术相比，计算时间可以减少 98%。我们还表明，分布式方法的性能几乎与集中式方法一样，在这封信中研究的设置中，误差在 6% 以内，其优点是所需时间显着减少 - 这是实际搜索任务中的一个重要考虑因素。