计算机工程与应用2019，Vol.55Issue(5)：219-225,250,8.DOI:10.3778/j.issn.1002-8331.1805-0175
多启发因素改进蚁群算法的路径规划
Path Planning Based on Improved Ant Colony Algorithm with Multiple Inspired Factor
摘要
Abstract
The path planning of mobile robots not only requires short path distances, but also avoids excessive turning of paths, serious bumps, and poor environmental adaptability. Therefore, this paper proposes improvement heuristics function based on three factors:path length, number of turns, and smoothness of gradient, comprehensively calculating of transi-tion probability. While improving the pheromone update method, it allocates the pheromone amount on each path accord-ing to the three-factor comprehensive index, guides ants to approach the path with the best overall performance. And it proposes a non-uniform initial pheromone method to prevent excessive ants into the dead end. It combines improved map modeling barriers to improve path safety. Simulation and experimental results show that the planning path obtained by the improved algorithm has a great improvement in the overall performance of the three factors, and has a good global search capability and convergence. Adjusting the parameters appropriately can also obtain a path with a prominent characteristic. Both the number of iterations and the calculation time perform better.关键词
蚁群算法/启发函数/路径规划/移动机器人/信息素Key words
Ant Colony Algorithm(ACA)/ inspired function/ grid path planning/ mobile robot/ pheromone分类
信息技术与安全科学引用本文复制引用
李理,李鸿,单宁波..多启发因素改进蚁群算法的路径规划[J].计算机工程与应用,2019,55(5):219-225,250,8.基金项目
国家自然科学基金(No.61672149) （No.61672149）
吉林省科技发展计划基金(No.20170520052JH) （No.20170520052JH）
吉林省教育厅"十三五"科学技术研究基金(No.2016097). （No.2016097）
