PFACO: Potential Field Ant Colony Optimization for Mobile Robot Path Planning

In response to the challenge of global path planning difficulties for automated guided vehicle in complex environments, traditional ant colony algorithms face several issues such as long search times, unordered search, and difficulty in finding the optimal route. We propose an optimized ant colony algorithm with potential field, named PFACO, that combines heuristic search and artificial potential field methods, along with the utilization of the pruning algorithm for secondary optimization of turning points. Specifically, we introduce improved potential fields and design heuristic functions to guide ants to swiftly navigate through unordered search states. After that, we incorporate the Triangle Pruning Algorithm (TPA) to perform secondary optimization by connecting turning points along the path. Simulation experiments demonstrate that PFACO, compared to both traditional Ant Colony Optimization (ACO) algorithms and other improved ACO algorithms, achieves an average improvement of 93% and 78% in convergence speed, and an average optimization of 25.26% and 3.25% in path length, respectively, across various map conditions. The results show that PFACO has better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.