基于改进混合蛙跳算法的动态环境路径规划

Path Planning in Dynamic Environment Based on Improved Shuffled Frog Leaping Algorithm

针对复杂场景中路径规划具有未知性和动态性,传统方法无法对路径规划问题进行求解的问题,设计一种改进混合蛙跳算法的机器人路径规划方法,以提高动态环境路径规划的求解精度.首先对动态环境路径规划的研究现状进行分析,并在此基础上建立数学模型;然后采用混合蛙跳算法对该模型进行求解,并针对基本混合蛙跳算法不足进行改进;最后对路径规划的有效性进行测试.测试结果表明,混合蛙跳算法可准确找到最优的路径规划方案,可应用于复杂场景路径规划中,且性能优于其他路径规划方法.

Aiming at the unknown and dynamic path planning in complex scene, the traditional method could not solve the problem of path planning, we designed an improved shuffled frog leaping algorithm for robot path planning in order to improve the accuracy of path planning in dynamic environment. Firstly, we analyzed the research status of dynamic environment path planning, and established its mathematical model. Secondly, we used the shuffled frog leaping algorithm to solve the model, and improved the shortage of basic shuffled frog leaping algorithm. Finally, we tested effectiveness of the path planning by simulation experiments. The results show that the proposed algorithm can accurately find the optimal path planning scenarios, and can be used in more complex scene path planning, and its performance is better than that of other path planning methods.