| 改进蚁群算法的局部信息动态路径规划 |
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| 引用本文： | 赵峰,杨春曦,陈飞,黄凌云,谈诚.改进蚁群算法的局部信息动态路径规划[J].计算机测量与控制,2017,25(8):130-134. |
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| 作者姓名： | 赵峰 杨春曦 陈飞 黄凌云 谈诚 |
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| 作者单位： | 昆明理工大学 化学工程学院, 昆明 650500,昆明理工大学 化学工程学院, 昆明 650500,昆明理工大学 化学工程学院, 昆明 650500,昆明理工大学 国土资源工程学院, 昆明 650093,昆明理工大学 国土资源工程学院, 昆明 650093 |
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| 基金项目： | 国家自然科学基金(61364002);云南省教育厅科学研究基金(2016YJS020)。 |
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| 摘 要： | 针对传统蚁群算法收敛速度慢、对动态路径变化适应性低的局限性,提出了一种基于局部信息获取策略的动态改进型蚁群算法。该算法利用局部信息获取策略,进行最优局部目标点的获取,然后调用改进蚁群算法获取局部区域内的最优路径,再重复循环获取新的最优局部目标点,直到找到全局目标点；与此同时,将提出的改进型蚁群算法应用于动态路径规划中的路径寻优与避障,仿真结果表明:提出的算法在具有与传统蚁群算法相当的路径优化效果的同时,能够有效适应障碍变化、大大提高了路径规划的收敛速度。
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| 关 键 词： | 蚁群算法 局部信息 局部目标点 动态路径规划 |
| 收稿时间： | 2017/1/19 0:00:00 |
| 修稿时间： | 2017/2/27 0:00:00 |
Local Information Dynamic Path Planning Based on Improved Ant Colony Algorithm |
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| Zhao Feng,Yang Chunxi,Chen Fei,Huang Lingyun and Tan Cheng.Local Information Dynamic Path Planning Based on Improved Ant Colony Algorithm[J].Computer Measurement & Control,2017,25(8):130-134. |
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| Authors: | Zhao Feng Yang Chunxi Chen Fei Huang Lingyun and Tan Cheng |
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| Affiliation: | Kunming University of Science and Technology, Faculty of Chemical Engineering, Kunming 650500, China,Kunming University of Science and Technology, Faculty of Chemical Engineering, Kunming 650500, China,Kunming University of Science and Technology, Faculty of Chemical Engineering, Kunming 650500, China,Kunming University of Science and Technology, Faculty of Land Resource Engineering, Kunming 650093, China and Kunming University of Science and Technology, Faculty of Land Resource Engineering, Kunming 650093, China |
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| Abstract: | Considering the limitation of traditional ant colony algorithm''s slowish convergence and bad self-adaptability to dynamic path change, a dynamic improved ant colony algorithm based on local information acquisition strategy is proposed in this paper. Firstly,The local information acquisition strategy is used to obtain the optimal local target point. Then, the improved ant colony algorithm is called to obtain the optimal path in the local region.And the new optimal local target point of the neighbor region is obtained by repeating the loop until the global target point is found. Moreover, the improved ant colony algorithm is applied to the path optimization and obstacle avoidance in dynamic path planning. The simulation results show that the new algorithm proposed not only has considerable path optimization performance compared with the traditional ant colony one, but also has self-adaptive capacity faced with time-vary obstacles and faster convergence speed. |
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| Keywords: | ant colony algorithm local information local target point dynamic path planning |
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