文献类型：期刊文献

中文题名：基于改进人工鱼群算法的机器人路径规划

英文题名：Robot Path Planning Based on Improved Artificial Fish Swarm Algorithm

作者：罗如学[1];陈妙娜[1];林继灿[1]

机构：[1]广东海洋大学寸金学院,湛江524000

年份：2020

卷号：20

期号：23

起止页码：9445

中文期刊名：科学技术与工程

外文期刊名：Science Technology and Engineering

收录：CSTPCD、、北大核心2017、北大核心

基金：广东海洋大学寸金学院重点建设学科(CJXK201801);广东海洋大学寸金学院研究团队项目(CJ18CXQX026)。

语种：中文

中文关键词：路径规划;人工鱼群算法;改进视觉范围;拥挤度因子

外文关键词：path planning;artificial fish swarm algorithm;improved visual range;crowding factor

中文摘要：为改进人工鱼群算法在路径规划中的寻优作用,利用改进视觉范围和拥挤度因子函数,提高鱼群算法在机器人路径规划中的寻优工作。在传统鱼群算法中,视觉范围是恒定不变的。视觉范围决定寻优的全局和局部工作,拥挤度因子对算法收敛性具有影响。同时,在传统鱼群算法中,每次都选取最优解来执行,在栅格环境中往往会导致全局最优和局部最优互扰,导致路径规划不合理,为此,利用改进视觉范围拥挤度因子,同时记录可行解,当存在鱼群找到目标点时,就记录下找到目标点的鱼群轨迹,形成路径规划的可行解,在可行解中,选取路径最短为最优,保证路径的规划的合理性。与传统鱼群算法对比,证实研究算法在路径规划中具有更好的寻优工作,通过MATLAB仿真实验,验证了算法的有效性和稳定性。

外文摘要：In order to improve the optimization function of artificial fish swarm algorithm in path planning, the improved visual range and crowding factor function were used to improve the optimization work of fish swarm algorithm in robot path planning. In the traditional fish swarm algorithm, the visual range is constant. The visual range determines the global and local work of the optimization, and the crowding factor has an effect on the convergence of the algorithm. At the same time, in the traditional fish school algorithm, the optimal solution is chosen to execute every time. And it leads to the global optimal and local optimal mutual interference in the grid environment, which leads to the unreasonable path planning. Therefore, by using the improved visual range crowding factor, the feasible solution was recorded at the same time. When the fish school found the target point, the track of the fish school finding the target point was recorded to form the path planning. In the feasible solution, the shortest path was the best to ensure the rationality of path planning. Compared with the traditional fish school algorithm, it is proved that the research algorithm has better optimization work in path planning. Through MATLAB simulation experiment, the effectiveness and stability of the algorithm were verified.