基于改进蚁群算法的星球探测机器人路径规划技术  

Planetary Rover Path-planning Based on Ant Colony Optimization Algorithm

作　　者：岳富占[1] 崔平远[1] 崔祜涛[1] 

机构地区：[1]哈尔滨工业大学深空探测基础研究中心,哈尔滨150080

出　　处：《控制与决策》2006年第12期1437-1440,共4页Control and Decision

摘　　要：对蚁群算法中蚂蚁的个体行为进行改进,解决了星球表面复杂环境下探测机器人的路径规划问题.在个体行为中加入目标导向行为、惯性行为和沿障碍行走行为,并进行加权融合,改进了传统的ACO算法,提高了算法的智能,保证了算法的全局收敛性.在蚁群算法规划的基础上提出一种紧绳算法,对蚁群算法的最后结果进行处理,最终给出了最优规划路径.最后通过仿真对该方法进行验证.In order to navigate through unstructured planetary environment autonomously, a path-planning algorithm based on ant colony optimization （ACO）, goal-oriented behavior, inertial behavior and obstacle-following behavior are added to ant individual of ACO, By executing behavior weighted fusion, ACO planning algorithm is improved and used to resolve planning problem of planetary rover. Furthermore, a tight-line algorithm is presented, to give a ,shortest path from start point to the exploration site by processing the path-planning result of ACO. The simulation result shows of the path planning algorithm.

关 键 词：星球探测机器人 路径规划 蚁群算法 行为融合 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]