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| 基于ACS算法的移动机器人实时全局最优路径规划 | |
| 引用本文： | 谭冠政, 贺欢, SLOMAN A. 基于ACS算法的移动机器人实时全局最优路径规划. 自动化学报, 2007, 33(3): 279-285. doi: 10.1360/aas-007-0279 |
| 作者姓名： | 谭冠政 贺欢 SLOMAN A |
| 作者单位： | 1.中南大学信息科学与工程学院，长沙 410083 |
| 基金项目： | Supported by National Natural Science Foundation of P. R. China （50275150） and National Research Foundation for the Doctoral Program of Higher Education of P. R. China （20040533035） The authors gratefully acknowledge the reviewers for their comments and suggestions which have led to the significant improvements of this paper. |
| 摘 要： | 以Ant Colony System（ACS）算法为基础提出了一种新的移动机器人实时全局最优路径规划方法.这种方法包括三个步骤：第一步是采用链接图理论建立移动机器人的自由空间模型，第二步是采用Dijkstra算法搜索出一条无碰撞次优路径，第三步是采用ACS算法对这条次优路径的位置进行优化，从而得到移动机器人的全局最优路径.计算机仿真实验的结果表明所提出的方法是有效的，可用于对移动机器人进行实时路径规划.仿真结果也证实了所提出的方法在收敛速度、解的波动性、动态收敛特征以及计算效率等方面都具有比采用精英保留遗传算法的移动机器人路径规划方法更好的性能. |
| 关 键 词： | 移动机器人 全局最优路径规划 ACS算法 链接图 Dijkstra算法 |
| 收稿时间： | 2005-08-31 |
| 修稿时间： | 2006-08-28 |
Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots |
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| TAN Guan-Zheng, HE Huan, SLOMAN Aaron. Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots. ACTA AUTOMATICA SINICA, 2007, 33(3): 279-285. doi: 10.1360/aas-007-0279 | |
| Authors: | TAN Guan-Zheng HE Huan SLOMAN Aaron |
| Affiliation: | 1. School of Information Science and Engineering, Central South University, Changsha 410083 P. R. China 2. Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100080 P. R. China 3. School of Computer Science, The University of Birmingham, Birmingham B15 2TT, The United Kingdom |
| Abstract: | A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path, and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model. |
| Keywords: | Mobile robot globally optimal path planning ACS algorithm MAKLINK graph Dijkstra algorithm |
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