PAC-MAPF: A Parallel Asynchronous Framework for Scalable Multi-Agent Path Finding Using Modern C++ Concurrency Patterns

The challenge of coordinating multiple autonomous agents in shared environments represents a fundamental bottleneck in contemporary robotics and automated systems. Current optimal Multi-Agent Path Finding (MAPF) algorithms, while theoretically sound, encounter severe practical limitations when deployed in real-world scenarios requiring coordination among hundreds of agents [15]. These limitations manifest primarily as exponential computational complexity and inadequate utilization of modern parallel hardware architectures. This paper introduces the Parallel Asynchronous Conflict-Search Framework for Multi-Agent Path Finding (PAC-MAPF), an innovative system designed to bridge the critical gap between algorithmic completeness and practical deployment scalability. The framework employs three interconnected technological advances: a lock-free priority management system for conflict resolution tasks [10], [11], a heuristic-aware distributed work scheduler that dynamically balances computational load [14], and a memory-optimized state representation engineered for cache efficiency [27]. Comprehensive evaluation across standardized benchmarks and novel large-scale scenarios demonstrates that the proposed framework achieves significant performance improvements over existing sequential and parallel approaches [2], [6]. Specifically, the system maintains solution quality within acceptable bounds while reducing computation time by an order of magnitude for problems involving hundreds of agents. These advances enable real-time path coordination at scales previously unattainable with optimal methods, representing a substantial step toward practical deployment in warehouse automation [22], mobile robotics, and intelligent transportation systems..