基于改进人工鱼群算法的机器人路径规划

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。

Robot path planning based on improved artificial fish swarm algorithm

In order to improve the optimization function of artificial fish swarm algorithm in path planning, the improved visual range and crowding factor function are 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, which often 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 is recorded at the same time. When the fish school finds the target point, the track of the fish school that finds the target point is recorded to form the path planning In the feasible solution, the shortest path is 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 are verified.