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.
出处
《控制与决策》
EI
CSCD
北大核心
2006年第12期1437-1440,共4页
Control and Decision
