IACO-DQ Path Planning for AGV in Complex and Dynamic Environments

This study addresses the problem of autonomous navigation and obstacle avoidance for mobile robots in complex and dynamically changing environments. A path planning strategy is proposed, namely the IACO-DQ (I-ACO and Dyna-Q) algorithm, which utilizes I-ACO (Improved Ant Colony Optimization) to construct an initial global optimal path framework. This is combined with the model learning and planning capabilities of the Dyna-Q algorithm, allowing the AGV to predict future states through the environment model during actual exploration, thus achieving online real-time dynamic path planning. The goal is to provide AGV with an efficient and flexible path planning solution to handle dynamic obstacles, ensuring safe and rapid task completion in ever-changing environments. Simulation results demonstrate that the IACO-DQ algorithm significantly improves the AGV's obstacle avoidance capabilities and path planning efficiency in dynamic environments, showcasing enhanced robustness and adaptability.