Autonomous Mobile Robots (AMRs) have become increasingly significance across various industries, with efficient path planning being a crucial component for their operation, particularly in dynamic and unpredictable environments. Although extensive research has been conducted on cloud-based robotics path planning, significant challenges remain in addressing the latency and adaptability required for real-time decision making. This paper presents a novel approach that integrates edge cloud-computing with an improved Ant Colony Optimization (ACO) algorithm to overcome these challenges. By decentralizing computational task to the edge, the proposed method not only reduces latency but also improved the speed and accuracy of route planning. The integration of a global path storage system further accelerates the process by enabling to reuse the optimized paths in the future planning scenarios, thus improving efficiency. Moreover, the proposed ACO algorithm introduces a reduced pheromone deposit strategy, which accelerates convergence and reduces the number of iterations needed to find the optimal path. Simulation results demonstrate that the proposed approach significantly outperforms conventional algorithms in terms of both adaptability and convergence speed, leading to shorter and more efficient paths in complex, dynamic environments. These findings suggest that this collaborative system of edge-cloud and ACO approach could be instrumental in advancing the deployment of AMRs in industrial applications where real-time path planning and adaptability are critical.