Alternating Target-Path Planning for Scalable Multi-Agent Coordination

2026-05-08

AI Summary

This work addresses the computational intractability of the combined target assignment and pathfinding (TAPF) problem in large-scale multi-agent systems by proposing an iterative optimization framework that decouples target assignment from path planning for the first time. The approach repeatedly invokes an efficient suboptimal MAPF solver—such as LaCAM—within a given time budget and leverages conflict feedback from path planning to identify bottleneck agents, dynamically refining the target assignment. By introducing a reallocation mechanism guided by MAPF-generated feedback, the method significantly enhances the scalability of TAPF while maintaining high solution quality. Experimental results demonstrate that the proposed approach substantially outperforms state-of-the-art CBS-based solvers in large-scale scenarios, exhibiting strong potential for real-world deployment.

Abstract

The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to agents. Prior work on TAPF has relied exclusively on Conflict-Based Search (CBS), which tightly couples target assignment and pathfinding, resulting in compute-intensive, non-scalable solutions. In contrast, we propose an iterative refinement framework that decouples target assignment from pathfinding. Our framework builds on modern, fast, suboptimal MAPF solvers, such as LaCAM. Specifically, within a given time budget, it repeatedly solves MAPF for the current target assignment, identifies bottleneck agents via MAPF feedback, and refines the assignment. Empirical results show that feedback-driven reassignment loop is effective, enabling our framework to scale well beyond the reach of the state-of-the-art CBS-based solver while maintaining decent solution quality. This represents a solid step toward practical, large scale TAPF suitable for real-world setups.

Problem

• multi-agent coordination
• target assignment
• pathfinding
• scalability
• TAPF

Innovation

• TAPF
• decoupled planning
• iterative refinement
• bottleneck-aware reassignment
• scalable multi-agent coordination