Path Planning for Robots Based on Adaptive Dual-Layer Ant Colony Optimization Algorithm and Adaptive Dynamic Window Approach

To address the limitations of ant colony optimization (ACO) algorithm in terms of convergence speed, search efficiency, local optimal traps, and dependence on high-precision maps, this paper proposes adaptive dual layer ant colony optimization (ADL-ACO) algorithm and adaptive dynamic windowing approach (ADWA) for dynamic path planning of robots. The ADL-ACO has a dual-layer structure, which is divided into a path planning layer and a trajectory optimization layer, where the AEACO generates collision-free initial paths and the TOA further optimizes the initial paths. The first layer is the Adaptive Elite Ant Colony Optimization (AEACO) algorithm for the path planning layer, which accelerates the convergence speed and enhances the global search capability through adaptive parameter tuning and pseudo-random state transition rules. The second layer is the Trajectory Optimization Algorithm (TOA) for the trajectory planning layer, which optimizes the initial path in terms of length, number of turns, safety, and smoothness, and utilizes the segmented B-spline technique to enhance the smoothness of the paths. Moreover, the Adaptive Dynamic Window Approach (ADWA) is proposed for dynamic obstacle avoidance in dynamic environments to enhance the adaptability of the algorithm in complex environments. The simulation results indicate that ADL-ACO reduces the length of optimal path, average path length, execution time, optimal path turning point, and smoothness in comparison to other algorithms, and ADWA improves the robot’s obstacle avoidance efficiency and safety. The experimental trials in real-world indoor and outdoor conditions validate the algorithm’s efficacy in this study. The method presented in this research provides a novel way to address the path planning for mobile robots.