基于改进蜣螂优化算法的搬运机器人路径规划

Path planning of handling robot based on improved dung beetle optimization algorithm

针对基本蜣螂优化算法在路径规划方面存在的路径较长、迭代次数多以及局部最优等问题,提出一种改进蜣螂优化算法。首先利用Chebyshev混沌映射优化其初始群体,以提升个体质量;其次将黄金正弦算法引入觅食蜣螂位置更新策略中,以提升算法的收敛速度,使得算法搜寻有效性得以提升;最后在偷窃蜣螂位置加入动态权重,以扩大算法的搜索范围,降低算法提前收敛的概率。利用栅格地图法建立环境地图,模拟搬运机器人的工作环境,利用MATLAB仿真软件,将改进蜣螂优化算法与基本蜣螂优化算法在不同的栅格地图中进行仿真。通过分析仿真数据得出在3种不同的栅格地图中改进蜣螂优化算法在平均路径长度上比基本蜣螂优化算法分别缩短了31.25%、10.31%和8.59%;在平均迭代次数上分别减少了22.26%、12.19%和26.24%;在平均拐点个数上分别减少了35.11%、9.52%和35.09%。仿真结果证明改进蜣螂优化算法在搬运机器人路径规划方面拥有更好的效果,验证了改进蜣螂优化算法的可行性。

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.