摘要
针对蜣螂优化算法(DBO)局部探索能力不足、路径规划不平滑且动态避障能力差的缺点,提出一种基于改进蜣螂算法和动态窗口算法(DWA)的避障路径规划算法。首先,针对DBO局部搜索能力不足的问题,引入Tent混沌映射初始化种群,并利用黄金正弦算法(GOLDSA)引入非线性权重优化蜣螂位置更新方式;然后,删除规划路径中的冗余节点,将优化DBO与DWA融合解决DBO算法的不足,并进行仿真实验。实验结果表明改进DBO相对于其他算法在转弯次数、最短路径、迭代次数方面分别减少40.0%、3.3%、92.0%,算法性能得到显著优化。改进DBO与DWA算法在保证避开静态障碍物的同时能够避开未知与动态障碍物,规划路线相对平滑,有效提升了机器人的运行效率和安全性。
Aiming at the disadvantages of Dung Beetle Optimizer(DBO),such as insufficient local exploration ability,unsmooth path planning and poor dynamic obstacle avoidance ability,an obstacle avoidance path planning algorithm has been proposed based on the DBO and Dynamic Window Approach(DWA)method.Firstly,to solve the problem of insufficient local exploration ability of DBO,Tent chaotic mapping is introduced to initialize the population,and Gold Sine Algorithm(GOLD-SA)is used to introduce nonlinear weights to optimize the position update of dung beetles.Then,the redundant nodes in the planned path are deleted,and the optimized DBO and DWA are integrated to solve the shortcomings of DBO algorithm,and simulation experiments are carried out.The experimental results show that the number of turns,the shortest path and the number of iterations of the improved DBO is reduced by 40.0%,3.3%and 92.0%,respectively,compared with other algorithms,and the performances of the algorithm are obviously optimized.The improved DWO integrated with and DWA algorithm can avoid unknown and dynamic obstacles,avoiding static obstacles at the same time,and the planned route is relatively smooth,which improves the operation efficiency and safety of the robot.
作者
范县成
凌新宇
朱国武
汤巍
FAN Xiancheng;LING Xinyu;ZHU Guowu;TANG Wei(School of Electrical and Electronic Engineering,Anhui Institute of Information Technology,Wuhu 241000,China)
出处
《四川轻化工大学学报(自然科学版)》
2025年第4期40-47,共8页
Journal of Sichuan University of Science & Engineering(Natural Science Edition)
基金
安徽省高等学校省级自然科学研究计划项目(2024AH050637)。
