Conflict Area Prediction for Boosting Search-Based Multi-Agent Pathfinding Algorithms

Abstract

We address the challenge of efficiently controlling multi-agent systems, crucial in fields like logistics and traffic management. We propose a novel approach that combines learning-based techniques with search-based methods, focusing on enhancing the conflict-based search (CBS). The CBS ensures optimality but suffers from increasing complexity as agents or maps grow. To tackle this, we leverage learning-based approaches to enhance computational efficiency. By training a conflict area prediction (CAP) network, we anticipate potential conflict areas, allowing for low-level path planners to explore conflict-free paths. Our experiments demonstrate the effectiveness of our method in reducing computational demands compared to existing approaches.