Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning

Abstract

Path planning in complex environments presents a substantial research challenge for mobile robots. This study introduces an enhanced ant colony algorithm based on islands (EACI) for mobile robot path planning. First, the original map’s grid cells—which could potentially cause ants to become trapped in deadlocks—are transformed into obstacles. This process generates an auxiliary map, where a specified number of islands are evenly distributed between the starting and end grids. Second, an irregular pheromone initialization strategy is employed to enhance the information transmission between neighboring islands. Concurrently, the heuristic function is refined, and an adaptive evaporation coefficient is incorporated to facilitate dynamic adjustments in pheromone updates. These modifications effectively reduce the number of iterations required and decrease the incidence of deadlock among the ants. Third, the performance and advantages of the EACI are validated in various grid maps. Simulation results demonstrate that, compared to other optimization algorithms, the EACI method provides superior path solutions, achieves faster convergence, and reduces the number of lost ants. In 20 × 20, 30 × 30, 40 × 40, and 50 × 50 environments, the average numbers of iterations are 1, 1.4, 6.2, and 7.1, respectively, while the average numbers of lost ants are 9.85, 27.5, 47.6, and 99.2, respectively—demonstrating strong stability and adaptability. Finally, real-world experiments validate the algorithm’s effectiveness.