Alternating Target-Path Planning for Scalable Multi-Agent Coordination

Researchers propose a decoupled iterative framework for multi-agent coordination that separates target assignment from pathfinding, achieving better scalability than existing conflict-based approaches. The method leverages fast suboptimal solvers like LaCAM and feedback-driven reassignment to handle larger agent systems while maintaining acceptable solution quality.

This research addresses a fundamental computational bottleneck in multi-agent systems—the concurrent problem of assigning distinct targets to agents while ensuring collision-free paths. Traditional approaches using Conflict-Based Search create tight coupling between assignment and pathfinding decisions, forcing the system to recompute paths whenever conflicts arise, which becomes prohibitively expensive as agent counts increase. The proposed framework inverts this strategy by decoupling the two problems, allowing them to be solved iteratively rather than monolithically. By building on modern suboptimal MAPF solvers like LaCAM, the approach trades some solution optimality for dramatic computational efficiency gains. The feedback loop mechanism identifies bottleneck agents—those causing repeated conflicts—and strategically reassigns their targets, addressing problems where they emerge rather than solving everything simultaneously. This pragmatic design reflects a mature understanding of real-world constraints: practitioners often prefer scalable good solutions over optimal ones that never finish computing. The empirical validation demonstrating scalability beyond existing state-of-the-art methods suggests practical viability for warehouse automation, traffic control, and robotic swarm coordination. The work exemplifies how algorithmic restructuring, rather than pure optimization, can unlock new capability tiers. For systems requiring coordination of 50+ agents, this approach opens previously inaccessible possibilities. Future research will likely explore tighter integration of assignment and pathfinding within this decoupled framework, and extensions to dynamic environments where targets or obstacles change during execution.

• →Decoupling target assignment from pathfinding enables significantly better scalability than monolithic conflict-based approaches.
• →Iterative refinement with feedback-driven reassignment effectively identifies and resolves coordination bottlenecks in multi-agent systems.
• →Modern suboptimal MAPF solvers like LaCAM provide the foundation for practical, large-scale agent coordination.
• →The framework prioritizes computational tractability and real-world applicability over solution optimality.
• →Results indicate viability for practical deployment in warehouse automation and robotic coordination tasks.