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Mixed-Integer Linear Programming Models for Multi-Robot Non-Adversarial Search
arXiv - CS - Computational Complexity Pub Date : 2020-11-25 , DOI: arxiv-2011.12480
Beatriz A. Asfora, Jacopo Banfi, Mark Campbell

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:

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。
更新日期:2020-11-27
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