基于改进蚁群算法的星球探测机器人路径规划技术

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．

Planetary Rover Path-planning Based on Ant Colony Optimization Algorithm

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.