改进人工鱼群算法的机器人路径规划及跟踪

为了使移动机器人在已知环境中规划出最优路径并对路径进行跟踪,提出了基于改进人工鱼群算法的机器人路径规划方法和PID跟踪方法。使用栅格法建立了环境模型;分析了传统人工鱼群算法原理,对算法的视觉范围、移动步长、可行走区域进行了重新定义,使之能够适用于栅格环境;提出了加权平均视觉范围和自适应拥挤度因子,兼顾了算法前期大范围搜索和后期细致搜索;使用改进人工鱼群算法优化PID控制参数;经实验,相比于传统算法,改进算法规划出的路径长度减少了11.4%,改进算法优化的PID参数在超调量、上升时间、震荡次数等方面优势明显。

Mobile Robot Path Planning and Tracking Based on Improved Artificial Fish Swarm Algorithm

To plan optimal path in the known environment for mobile robot, path panning and tracking method based on improved artificial fish swarm algorithm is proposed. Grid method is used to model the environment, traditional algorithm is analyzed. Visual range, step length, can-walking area is redefined to st to grid environment. Weighted average visual range and adaptive crowding factor are raised, which balances large-scope searching at earlier stage and careful searching at later stage. PID tracking parameters are optimized by improved artificial fish swarm algorithm. By trial, compared with algorithm, path planned by improved algorithm decreased by 11.4%, and PID parameters optimized by improved algorithm possess advantages of overshoot, rise time, and null.