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基于病态参数分离的机器人运动学标定测量构型优化

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针对一种高灵巧性机器人及其连杆参数高敏感性与高定位精度需求,为解决机器人运动学标定随机测量构型存在绝对定位精度低、参数辨识效果及标定结果鲁棒性较差的问题,提出一种病态参数分离与DETMAX-改进差分进化(DETMAX-IDE)算法的机器人运动学标定测量构型分步优化方法.首先,建立机器人位置误差模型.其次,建立一种可观性综合指标,评价不同机器人标定测量构型的总体可观测性和灵敏度.最后,分离机器人运动学位置误差模型的病态参数,建立测量构型优化目标函数和约束条件,提出一种基于DETMAX算法与改进差分进化算法结合的分步迭代优化算法(简称为DETMAX-改进差分进化算法,简写为DETMAX-IDE算法),开展机器人运动学标定测量构型分步迭代优化.通过机器人运动学标定仿真与实验,验证了所提方法的有效性.实验结果表明,与随机测量构型相比,所提方法对应的机器人绝对定位精度的平均值和均方差分别降低了 62.09％和 62.45％.
Ill-conditioned parameter separation based optimization of measurement configuration for robot kinematic calibration
For the requirement of a high dexterity robot with high sensitivity of link parameters and precise positioning accuracy,there are the problems of low absolute positioning accuracy,poor parameter identification effectiveness,and calibration robustness in the random measurement configuration of robot kinematic calibration.To address these issues,a robot kinematic calibration measurement configuration stepwise optimization method based on ill-conditioned parameter separation and DETMAX and improved differential evolution(DETMAX-1DE)algorithm is proposed.Firstly,a robot position error model is formulated.Secondly,a comprehensive observability index is developed to evaluate the overall observability and sensitivity for different robot calibration measurement configurations.Finally,ill-conditioned parameters of the robot kinematic position error model are separated.The objective function and constraint conditions are established for optimizing the measurement configuration,the differential evolution algorithm is improved(abbreviated as IDE algorithm),and a step-by-step iterative optimization algorithm based on the DETMAX algorithm and IDE algorithm is presented,which is referred to as DETMAX-improved differential evolution algorithm,and abbreviated as DETMAX-IDE algorithm.The step-by-step iterative optimization of robot kinematic calibration measurement configuration is achieved.Using numerical simulation and experimental robot kinematic calibration,the effectiveness of the proposed method is evaluated.Compared with the random measurement configuration,the experimental results show that the average and the mean square deviation of the robotic absolute positioning accuracy corresponding to the proposed method are improved,with an decrease of 62.09％and 62.45％,respectively.

robot kinematic calibrationmeasurement configuration optimizationill-conditioned parameter separationDETMAX-IDE algorithmposition error model

郭万金、李锦辉、郝钦磊、曹雏清、赵立军

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长安大学道路施工技术与装备教育部重点实验室 西安 710064

芜湖哈特机器人产业技术研究有限公司芜湖 241007

埃夫特智能装备股份有限公司 芜湖 241060

长三角哈特机器人产业技术研究院芜湖 241007

哈尔滨工业大学机电工程学院 哈尔滨 150000

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机器人运动学标定 测量构型优化 病态参数分离 DETMAX-IDE算法 位置误差模型

国家自然科学基金面上项目中央高校基本科研业务费专项安徽省博士后研究人员科研活动项目安徽省教育厅科学研究重点项目中国博士后科学基金

522750053001022532012023B675KJ2020A03642022M722435

2024

10.19650/j.cnki.cjsi.J2312263

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心
影响因子：2.372
ISSN：0254-3087
年,卷(期)：2024.45(2)
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