关键词: 搬运机器人, 蜣螂优化算法, 路径规划, Chebyshev混沌映射, 黄金正弦算法, 动态权重, 栅格地图

Abstract: Aiming at the problems of long path,large number of iterations and local optimization of the basic dung beetle optimization algorithm in path planning,an improved dung beetle optimization algorithm was proposed. Firstly,Chebyshev chaos mapping was used to optimize its initial population in order to improve individual quality. Secondly,the golden sinusoidal algorithm was introduced into the position update strategy of foraging dung beetle,which can improve the convergence speed of the algorithm and improve the effectiveness of the algorithm search. Finally,dynamic weights were added to the stealing dung beetle position to expand the search range of the algorithm and reduce the probability of the algorithm convergence in advance.Using the raster map method to establish the environment map,simulating the working environment of the handling robot,using MATLAB simulation software,the improved dung beetle optimization algorithm and the basic dung beetle optimization algorithm were simulated in different raster maps,and the improved dung beetle optimization algorithm in three different raster maps was obtained by analyzing the simulation data,which was 31.25 %,10.31 % and 8.59 % shorter than the basic dung beetle optimization algorithm in terms of average path length,respectively. The average number of iterations decreased by 22.26 %,12.19 % and 26.24 %. The average number of inflection points decreased by 35.11 %,9.52 % and 35.09 %.It is proved that the improved dung beetle optimization algorithm has better effect in the path planning of the handling robot,and the feasibility of the improved dung beetle optimization algorithm is verified.

Key words: handling robot, dung beetle optimization algorithm, path planning, Chebyshev chaos mapping, golden sinusoidal algorithm, dynamic weights, raster map