提出解耦目标分配与路径规划的新框架，实现大规模多智能体协调的高效求解。
Alternating Target-Path Planning for Scalable Multi-Agent Coordination
- 将目标分配与路径规划分离，通过反馈迭代优化分配方案。
- 在限定时间内显著提升求解规模，支持超过现有方法的智能体数量。
- 适合需要高扩展性的实际多智能体系统，如仓储机器人调度。
并发目标分配与路径规划（TAPF）问题在多智能体路径规划（MAPF）基础上，要求为各智能体分配不同目标并生成无碰撞路径。以往工作仅依赖冲突检测搜索（CBS），其目标分配与路径规划高度耦合，导致计算开销大、难以扩展。本文提出一种迭代优化框架，解耦目标分配与路径规划，基于快速的次优MAPF求解器（如LaCAM）构建。在给定时间预算内，反复求解当前分配下的MAPF，通过反馈识别瓶颈智能体并动态调整分配。实验表明，该反馈驱动的重分配循环有效，使框架可扩展至远超当前最优CBS方法的规模，同时保持良好解质量。这为实际应用中大规模TAPF提供了可行路径。
原文摘要 · Abstract (English)
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.
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