High-safety path optimization for mobile robots using an improved ant colony algorithm with integrated repulsive field rules

Abstract

This paper introduces an improved ant colony algorithm, AR-ACO (A*-Repulsive field-ACO), to enhance the efficiency of path planning for mobile robots in complex maps. This paper adopts six strategies to optimize the iterative process of the ACO algorithm and strengthen the selection of the optimal path. Firstly, inspired by the repulsive field characteristics of artificial potential fields, an Obstacle Impact Factor is introduced to identify the risk zones in the map and adjust the initial distribution of pheromones. Secondly, to ensure the global and discriminative nature of the map, a dynamic constraint is applied to pheromones, following the MAX-MIN Ant system. Thirdly, a novel backtracking mechanism is proposed to address deadlock situations and reduce computational burden. Fourthly, heuristic information in the ant colony algorithm is improved to accelerate convergence speed and enhance the smoothness of global paths. Fifthly, the evaporation factor pheromone formula has been improved to optimize the ability to cope with complex terrains. Additionally, an improved elite ant retention strategy is introduced to significantly enhance the ants' optimization capability while ensuring convergence speed. Simulation experiments and physical verifications conducted in various environments, especially in complex large-scale maps, demonstrate that the optimized algorithm outperforms traditional algorithms, confirming the effectiveness of the improved ant colony algorithm.