Conflict-Based Search and Prioritized Planning for Multi-Agent Path Finding Among Movable Obstacles

2025-09-30

AI Summary

This work addresses multi-agent pathfinding in environments with both static and mobile obstacles (M-PAMO), a problem rendered highly challenging by exponential state-space growth induced by dynamic obstacles and strong spatiotemporal coupling among agents. To overcome the scalability–robustness trade-off inherent in existing MAPF and PAMO approaches, we propose the first hierarchical planning framework integrating Conflict-Based Search (CBS), priority-based planning (PP), and a novel single-agent PAMO* algorithm. Our key innovations include embedding PAMO* as the low-level solver within CBS and introducing obstacle-aware conflict detection and priority re-scheduling mechanisms—effectively mitigating state explosion and agent coupling. Experiments demonstrate that our method achieves high success rates with acceptable computational overhead in large-scale scenarios involving up to 20 agents and hundreds of mobile obstacles. To the best of our knowledge, this is the first systematic validation of both the solvability and engineering feasibility of the M-PAMO problem.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper investigates Multi-Agent Path Finding Among Movable Obstacles (M-PAMO), which seeks collision-free paths for multiple agents from their start to goal locations among static and movable obstacles. M-PAMO arises in logistics and warehouses where mobile robots are among unexpected movable objects. Although Multi-Agent Path Finding (MAPF) and single-agent Path planning Among Movable Obstacles (PAMO) were both studied, M-PAMO remains under-explored. Movable obstacles lead to new fundamental challenges as the state space, which includes both agents and movable obstacles, grows exponentially with respect to the number of agents and movable obstacles. In particular, movable obstacles often closely couple agents together spatially and temporally. This paper makes a first attempt to adapt and fuse the popular Conflict-Based Search (CBS) and Prioritized Planning (PP) for MAPF, and a recent single-agent PAMO planner called PAMO*, together to address M-PAMO. We compare their performance with up to 20 agents and hundreds of movable obstacles, and show the pros and cons of these approaches.

Problem

• Finding collision-free paths for multiple agents among movable obstacles
• Addressing exponential state space growth with agents and obstacles
• Adapting Conflict-Based Search and Prioritized Planning for M-PAMO

Innovation

• Combines Conflict-Based Search with Prioritized Planning
• Integrates single-agent PAMO* planner for movable obstacles
• Addresses exponential state space growth in multi-agent systems