A Variable Size Grid Based Ant Colony Optimization Algorithm for Robot Path Planning

Authors

  • Xu Guo, Wenlong Ji, Siquan Li, Anqi Xu, Yanling Shang, and Fangzheng Gao School of Automation, Nanjing Institute of Technology, Nanjing 211167, China Author

Abstract

—In this study, we propose an enhanced Ant Colony Optimization algorithm embedded within a variable-resolution grid framework to improve the performance of mobile robot path planning. To address the typical limitations of conventional ACO methods—namely, their slow convergence, tendency to get trapped in local optima, and inability to produce smooth trajectories—the algorithm incorporates a series of refined strategies grounded in grid-based representation.Firstly, an adaptive grid partitioning strategy with variable resolution is developed: it enlarges the search neighborhood in com plex, obstacle-dense regions and contracts it in open areas, thereby achieving a balance between search efficiency and path quality. Secondly, a refined heuristic function, drawing inspiration from the A* algorithm, is employed to guide ants more effectively toward the goal. The A* algorithm is also leveraged to initialize the pheromone distribution, which helps accelerate convergence during the early iterations. Moreover, a directional bias mechanism is integrated to suppress redundant node exploration and improve search efficiency. To further enhance the algorithm’s global exploration capability and avoid premature convergence, a dynamic pheromone update scheme is proposed—featuring both a reward-penalty model and an adaptive evaporation rate.Comprehensive simulations carried out in MATLAB evaluate the performance of the proposed approach in comparison with the standard ACO, various enhanced ACO versions, and other intelligent optimization techniques, including Genetic Algorithm , Particle Swarm Optimization, and Differential Evolution. The results consis tently demonstrate that the modified algorithm achieves more efficient convergence, produces shorter and smoother paths, and exhibits greater robustness—highlighting its strong potential for practical applications and future hardware integration.

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Published

2025-09-01

How to Cite

A Variable Size Grid Based Ant Colony Optimization Algorithm for Robot Path Planning. (2025). IAENG International Journal of Applied Mathematics, 55(9), 2847-2856. https://ijesworld.com/index.php/IEANG/article/view/97