<![CDATA[In recent years, Automated Mobile Robots (AMRs) have gained significant attention in industry and research applications, requiring efficient path-planning algorithms to optimize task performance. While widely adopted, conventional Ant Colony Optimization (ACO) algorithms suffer from low convergence rates and delays in task execution, particularly in dynamic environments due to insufficient exploration of this context. However, traditional Ant Colony Optimization (ACO) algorithms, widely used for AMR path planning, exhibit limitations such as low convergence rates and redundant recalculations, particularly in environments with frequently changing obstacles. To address these challenges, this study proposes an Integrative Edge Cloud-Based Ant Colony Optimization (IECACO) algorithm. IECACO incorporates a novel path retrieval mechanism and edge cloud computing infrastructure to minimize redundant path computation and improve convergence efficiency. The proposed algorithm is tested within a simulated 2D occupancy grid environment using both a 4×4 map for controlled experiments and a 20×20 map for comparative evaluation against a prior Improved ACO (IACO) study. Experimental simulation results, based on 50 independent runs in settings, demonstrate that IECACO achieves at least 4.76% reduction compared to traditional ACO. Based on the observation of 10 independent runs between IECACO and IACO, IECACO leading a significant reduction in both static and dynamic settings. Although this study is conducted in a simulated environment, the findings lay a foundation for future real-world implementations.]]>
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