Preference-based multi-objective multi-agent path finding

Abstract

Multi-Agent Path Finding (MAPF) is a well-studied problem that aims to generate collision-free paths for multiple agents while optimizing a single objective. However, many real-world applications require the consideration of multiple objectives. In this paper, we address a novel extension of MAPF, Multi-Objective MAPF (MOMAPF), that aims to optimize multiple given objectives while computing collision-free paths for all agents. In particular, we aim to incorporate the preferences of a decision maker over multi-agent path planning. Thus, we propose to solve a scalarized MOMAPF, whereby the given preferences of a decision maker are reflected by a weight value associated to each given objective and all weighted objectives are combined into one scalar. We introduce a solver for scalarized MOMAPF based on Conflict-Based Search (CBS) that incorporates an adapted path planner based on an evolutionary algorithm, the Genetic Algorithm (GA). We also introduce three practical objectives to consider in path planning: efficiency, safety, and smoothness. We evaluate the performance of our proposed method in function of the input parameters of GA on experimental simulations and we analyze its efficiency in providing conflict-free solutions within a fixed time.