摘要
为解决机器人在多约束条件下路径寻优能力差、搜索算法收敛速度慢等问题,提出了一种基于改进遗传算法的机器人路径规划方法。首先,利用栅格法构建机器人工作环境,并以路径长度、平滑度和路径困难度为约束条件构建模型;其次,通过增加删除算子、平滑算子对传统遗传算法进行改进,并引入小生境法避免算法陷入早熟;最后,通过对比实验验证所提算法的性能。实验结果表明,所提方法能够在多约束条件下有效处理路径规划问题并找到最优路径,且与其他方法相比,所提方法在路径长度、平滑度、路径困难度以及运行时间等方面均具有相应的优势。
To solve the problems of poor path-finding ability and slow convergence of search algorithm under multiple constraints, a robot path planning method based on improved genetic algorithm was proposed.Firstly, the grid method is used to construct the robot working environment, and the model is constructed with the constraints of path length, smoothness and path difficulty.Secondly, the traditional genetic algorithm is improved by adding deletion operators and smoothing operators, and introducing niches method to avoid the algorithm from falling into precociousness.Finally, the performance of the proposed algorithm is verified by comparative experiments.The experimental results show that the proposed method can effectively deal with the path planning problem and find the optimal path under multiple constraints, and compared with other methods, the proposed method has better performance in terms of path length, smoothness, path difficulty and running time with a corresponding advantages.
作者
常见
任雁
CHANG Jian;REN Yan(Department of Information and Art Design,Henan Forestry Vocational College,Luoyang 471002,China;Henan Orchard Management Special Robot Engineering Technology Research Center,Henan Forestry Vocational College,Luoyang 471002,China)
出处
《组合机床与自动化加工技术》
北大核心
2023年第2期23-27,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学青年基金项目(51305128)。
