基于随机森林贝叶斯优化的机械臂运动参数优化研究  

Research on kinematic parameters optimization of robot arm based on random forest Bayesian optimization

作　　者：刘晨晖 张为民[1] 薛峰 LIU Chenhui;ZHANG Weimin;XUE Feng(School of Mechanical Engineering,Tongji University,Shanghai 201804,CHN)

机构地区：[1]同济大学机械与能源工程学院,上海201804

出　　处：《制造技术与机床》2023年第1期83-90,共8页Manufacturing Technology & Machine Tool

摘　　要：机械臂作为生产系统中的重要物流设备之一,在新产品投产前,需要对其未知的上下料轨迹及其运动参数进行寻优配置或整定,以找到适应于该产品的最优物流参数配置方案;但目前在机械臂的运动参数优化方面仍存在着优化时间成本较高、优化效果欠佳的问题。面向机械臂安装燃料电池极板工作场景,以机械臂的运动平稳性、绝对定位精度、物流效率的综合归一值为优化目标,采用基于随机森林概率代理模型的贝叶斯优化算法,以ABB IRB 1410机械臂为例,对其关键运动参数进行寻优。将其与高斯过程代理的贝叶斯算法在相同条件下所得实验结果进行了对比。实验表明,在机械臂运动参数优化问题上,随机森林贝叶斯算法相较于高斯贝叶斯算法,综合效果提升了15%。As one of the essential logistics equipment in the production system, the robot arm is required to find the optimal configuration for its unknown loading and unloading trajectory and its kinematic parameters before the new product is put into production to find the optimal logistics parameter configuration scheme for the production. However, there are still some problems in optimizing the kinematic parameters of the robot arm, such as high optimization time cost and poor optimization effect. This paper is aimed at the working scenario of installing the fuel cell plate on the ABB IRB 1410 robot arm.The integrated normalized value of the motion stationarity, absolute positioning accuracy, and logistics efficiency of the robot arm is taken as the optimization objective. The Bayesian optimization based on the random forest probability surrogate model is adopted to optimize the critical kinematic parameters.The optimal experimental results are compared with Bayesian optimization based on the Gaussian process surrogate model under the same conditions. The experimental results show that, compared with Gaussian process Bayesian optimization, the comprehensive effect of random forest Bayesian optimization is improved by 15% in optimizing robot arm kinematic parameters.

关 键 词：机械臂 参数优化 随机森林 高斯过程 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]