This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk. This generalization, referred to as multi-objective MAPF (MOMAPF), arises in several applications ranging from hazardous material transportation to construction site planning. In this paper, we present a new multi-objective conflict-based search (MO-CBS) approach that relies on a novel multi-objective safe interval path planning (MO-SIPP) algorithm for its low-level search. We first develop the MO-SIPP algorithm, show its properties and then embed it in MO-CBS. We present extensive numerical results to show that (1) there is an order of magnitude improvement in the average low level search time, and (2) a significant improvement in the success rates of finding the Pareto-optimal front can be obtained using the proposed approach in comparison with the state of the art. Finally, we also provide a case study to demonstrate the potential application of the proposed algorithms for construction site planning.

中文翻译：

---

使用安全区间路径规划的多目标基于冲突的搜索

本文解决了众所周知的多智能体路径寻找 (MAPF) 问题的泛化，该问题同时优化多个相互冲突的目标，例如旅行时间和路径风险。这种概括称为多目标 MAPF (MOMAPF)，出现在从危险材料运输到施工现场规划的多种应用中。在本文中，我们提出了一种新的基于多目标冲突的搜索 (MO-CBS) 方法，该方法依赖于一种新颖的多目标安全区间路径规划 (MO-SIPP) 算法进行低级搜索。我们首先开发了 MO-SIPP 算法，展示了它的特性，然后将它嵌入到 MO-CBS 中。我们提供了大量的数值结果来表明 (1) 平均低级搜索时间有一个数量级的改进，(2) 与现有技术相比，使用所提出的方法可以显着提高找到帕累托最优前沿的成功率。最后，我们还提供了一个案例研究，以证明所提出的算法在施工现场规划中的潜在应用。