基于改进狼群算法的移动机器人路径规划

Path planning of mobile robot based on improved wolf pack algorithm

为了获取机器人全局最优路径,文章提出一种基于改进的狼群算法移动机器人路径规划。首先运用栅格法对机器人环境进行建模;然后提出一种改进的狼群算法,该算法提出并行游走机制,进一步提高探狼的局部搜索能力;构建智能奔袭行为,提高猛狼自适应调节能力;提出向心围攻策略,使得算法收敛到全局最优。6类测试函数的仿真结果表明,改进的算法在局部搜索能力和自适应调节能力更强、收敛精度更高、收敛速度更快。移动机器人路径规划的仿真实验所涉及到的参数较多,文章利用Taguchi方法的三因素三水平正交试验法选取了最佳的参数组合。最后将改进的狼群算法和狼群算法都进行路径规划的仿真实验,结果表明,改进的算法在解决机器人路径规划问题上更有效。

In order to obtain the global optimal path of the robot, this paper proposes a path planning of mobile robot based on improved wolf pack algorithm. Firstly, the grid method is used to model the environment of robot. Then an improved wolf pack algorithm is put forward. In this algorithm, in order to further improve the local search ability of scout wolf, the parallel walk mechanism is proposed;in order to improve the adaptive adjustment ability of fierce wolf, the intelligent raid behavior is built;in order to converge to the global optimum, the centripetal siege strategy is put forward. Simulation results of six types of test functions show that the improved wolf pack algorithm has higher convergence accuracy, better search and adaptive adjustment abilities, and faster convergence speed. Many parameters involve in the centripetal simulation experiment of mobile robot path planning. The three-factor and three-level orthogonal experiment in Taguchi method is used to select the optimal parameter set. Finally, the improved wolf pack algorithm and the original wolf pack algorithm are used to do the path planning simulation experiment. The results show that the improved algorithm is more effective in solving the robot path planning problem.