基于改进MOPSO算法的分拣机器人尺度优化  

Scale optimization of sorting robot based on improved MOPSO algorithm

作　　者：黄金凤 李文 李德胜 刘照普 HUANG Jinfeng;LI Wen;LI Desheng;LIU Zhaopu(College of Mechanical Engineering,North China University of Technology,Tangshan 063210,China)

机构地区：[1]华北理工大学机械工程学院,河北唐山063210

出　　处：《传感器与微系统》2025年第4期151-155,共5页Transducer and Microsystem Technologies

摘　　要：为提高球团分拣机器人整体动力学性能,对本体机构结构参数进行尺度优化设计。首先,将机构关节驱动力矩峰值和总能耗最小作为评价指标,相邻杆长比例为优化变量;为增强粒子的多样性和收敛性,根据迭代次数引入自适应惯性权重和位置分裂策略,对多目标粒子群优化(MOPSO)算法予以改进并求解,并通过标准测试函数与标准MOPSO算法和NSGA-Ⅱ进行对比,验证改进MOPSO算法的优越性;最后,应用于球团分拣机器人尺度优化设计。结果表明:改进MOPSO算法得到的解优于标准MOPSO算法。In order to improve the overall dynamic performance of pellet sorting robot,scale optimization is carried out on the structural parameters of the body mechanism.Firstly,the peak of driving torque of the mechanism joint and the minimum total energy consumption are used as evaluation indicators,and the ratio of adjacent rod lengths is used as the optimization variable.To enhance the diversity and convergence of particles,the adaptive inertial weight and position splitting strategy are introduced according to the number of iterations,and the multi-objective particle swarm optimization(MOPSO)algorithm is improved and solved.The superiority of the improved MOPSO algorithm is verified by comparing the standard test function with the standard MOPSO algorithm and NSGA-Ⅱ.Finally,it is applied to the scale optimization design of pellet sorting robot.The results show that the solution of the obtained by the improved MOPSO algorithm is prior to the standard MOPSO algorithm.

关 键 词：球团分拣机器人 改进多目标粒子群优化算法 尺度优化 动力学性能 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]