于密度峰值聚类并行麻雀搜索算法的食品机器人路径规划

目的：提高食品拣取机器人工作效率。方法：提出了一种基于密度峰值聚类并行麻雀搜索算法的食品拣取机器人路径规划方法。建立以总移动距离、点位间路径平滑度和移动安全度为评价指标的食品拣取机器人路径规划模型，在保证机器人移动安全的同时，尽可能提升路径平滑度和降低移动距离。设计密度峰值聚类优化麻雀搜索算法(DSSA)，利用改进的密度峰值聚类算法对麻雀种群进行聚类分析，并根据聚类结果划分不同子族群和定义麻雀迭代进化方式；结合多点位路径规划模型和点位间存在的4条潜在移动路径，重新定义麻雀编码方式，搭建并行计算架构，以提高DSSA求解路径规划模型的精度和运算效率。结果：仿真结果表明，相比于其他食品机器人路径规划方法，总移动距离减少了7.3%~39.2%，移动时间降低了26.7%~50.1%。结论：所提方法能够明显改善食品分拣机器人路径规划效率，对提升食品加工企业生产效率具有一定的应用价值。

Path planning of food robot based on density peak clustering parallel sparrow search algorithm

Objective:In order to improve the efficiency of multi-point moving path planning of food picking robot, a path planning method of food picking robot based on density peak clustering parallel sparrow search algorithm is proposed.Methods:The path planning model of food picking robot was established with the total moving distance, path smoothness between points and moving safety as the evaluation indexes. While ensuring the moving safety of the robot, the path smoothness was improved and the moving distance was reduced as much as possible. The density peak clustering sparrow search algorithm (DSSA) was designed, as the improved density peak clustering algorithm was used to cluster the sparrow population, divided different sub populations and defined the sparrow iterative evolution mode according to the clustering results. Combined with the multi-point path planning model and the four potential moving paths between points, the sparrow coding mode was redefined and a parallel computing architecture was build to improve the accuracy and operation efficiency of DSSA solving the path planning model.Results:The simulation results showed that compared with other food robot path planning methods, the total moving distance was reduced by 7.3%~39.2% and the moving time was reduced by 26.7%~50.1%.Conclusion:The proposed method can significantly improve the path planning efficiency of food sorting robot, which has certain application value for improving the production efficiency of food processing enterprises.