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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多机器人非专家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有效搜索路径规划（MESPP）问题，其中，一组机器人被部署在以图形表示的环境中，以在给定的期限内捕获移动目标。我们证明了该问题是NP难的，并提出了解决MESPP问题的第一组混合整数线性规划（MILP）模型。我们的模型是第一个同时包含多个搜索器，任意捕获范围和误报的模型。尽管MESPP的最新算法基于简单的路径枚举，但采用MILP作为规划范式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，从而延长规划时间。这些模型旨在离线计算最佳解决方案，但可以轻松地采用分布式在线方法。我们的仿真表明，与以前的最新技术相比，可以将计算时间减少98％。我们还表明，在本文研究的设置中，分布式方法的性能几乎与集中式方法相当，在6％以内，其优点是所需的时间大大减少-这是实际搜索任务中的重要考虑因素。