Research Article
A Highly Efficient ACO-SA Algorithm for Robot Path Planning
@ARTICLE{10.4108/airo.11177, author={Wei Li and Feng Yang and Zhibin Li and Juan Mou and Yanmei Ha and Yi Liu and Yanmei Qin and Peiyang Wei and Linlin Chen and Xun Deng and Tinghui Chen and Jia Liu and Jianhong Gan and ZhenZhen Hu and Yonghong Deng and Guodong Li and Qifeng Su}, title={A Highly Efficient ACO-SA Algorithm for Robot Path Planning}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={5}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2025}, month={12}, keywords={Path Planning, Mobile Robot, Ant Colony Algorithm, Simulated Annealing Algorithm, Grid Modeling, ACO-SA}, doi={10.4108/airo.11177} }- Wei Li
Feng Yang
Zhibin Li
Juan Mou
Yanmei Ha
Yi Liu
Yanmei Qin
Peiyang Wei
Linlin Chen
Xun Deng
Tinghui Chen
Jia Liu
Jianhong Gan
ZhenZhen Hu
Yonghong Deng
Guodong Li
Qifeng Su
Year: 2025
A Highly Efficient ACO-SA Algorithm for Robot Path Planning
AIRO
EAI
DOI: 10.4108/airo.11177
Abstract
Intelligent algorithms continue to develop, and efficient path planning for mobile robots in complex environments relies heavily on such algorithms. This article proposes an ACO-SA path planning method that combines ant colony and simulated annealing to solve the problems of slow iteration and long computation in classical ant colony algorithms. The training base map is gridded and modeled, and the path is initially calculated and parameterized using traditional ant colony algorithms. The simulated annealing cooling mechanism is introduced to optimize the pheromone strategy, and the robustness of the algorithm is tested using a multi-modal large model random map. Simulation shows that under the map of the training base, the path length of the ACO-SA algorithm remains unchanged, and the convergence speed is improved by 88.9% and 58.3% respectively, while the running time is shortened by 1.5% and 3.5% respectively; In the worst results of the random map, the shortest path is shortened by 36.57% and 35.95% respectively compared to the traditional ant colony algorithm. This algorithm has better optimization effect and path stability, providing a practical solution for intelligent detection robot path planning.
Copyright © 2025 Wei Li et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
