Multi-Agent Path Finding (MAPF) remains a fundamental challenge in robotics, where classical centralized approaches exhibit exponential growth in joint-state complexity as the number of agents increases. This paper investigates Quadratic Unconstrained Binary Optimization (QUBO) as a structurally scalable alternative for simultaneous multi-robot path planning. This approach is a robotics-oriented QUBO formulation incorporating BFS-based logical pre-processing (achieving over 95% variable reduction), adaptive penalty design for collision and constraint enforcement, and a time-windowed decomposition strategy that enables execution within current hardware limitations. An experimental evaluation in grid environments with up to four robots demonstrated near-optimal solutions in dense scenarios and favorable scaling behavior compared to sequential classical planning. These results establish a practical and reproducible baseline for future quantum and quantum-inspired multi-robot coordinations.

翻译：多智能体路径规划（MAPF）一直是机器人学中的一个基础性挑战，传统的集中式方法在智能体数量增加时，其联合状态复杂度呈指数级增长。本文研究了二次无约束二进制优化（QUBO）作为一种结构上可扩展的替代方案，用于同步多机器人路径规划。该方法提出了一种面向机器人学的QUBO公式，其中融合了基于广度优先搜索的逻辑预处理（实现了超过95%的变量削减）、用于碰撞与约束执行的自适应惩罚设计，以及一种时间窗口分解策略，使得该方案能够在当前硬件限制内执行。在网格环境中对多达四个机器人进行的实验评估表明，在密集场景中该方法能获得接近最优的解，并且相较于传统的顺序规划方法展现出良好的扩展性。这些结果为未来基于量子计算及受量子启发的多机器人协调研究建立了一个实用且可复现的基准。