GPU-based Parallel Collision Detection for AGV Path Planning

The growing deployment of Automated Guided Vehicles (AGVs) in industrial and logistical environments demands efficient and scalable collision detection mechanisms to ensure safe and uninterrupted operation. Traditional CPU-based methods struggle to meet the real-time performance requirements of large-scale, dynamic AGV systems. This paper presents a parallel collision detection algorithm specifically optimized for execution on Graphics Processing Units (GPUs). By leveraging the inherent parallelism of GPU architectures, our approach significantly accelerates proximity checks and collision resolution among multiple AGVs operating in complex environments. We explore data-parallel strategies, including spatial partitioning and bounding volume hierarchies, to manage computational load and reduce latency. Performance evaluations conducted on AGV fleets show substantial speed-up — up to 30x compared to OpenMP-based CPU implementations — while maintaining high detection accuracy. The results highlight the potential of GPU-accelerated collision detection to enhance responsiveness and safety of next-generation AGV systems.