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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (8): 1655-1666    DOI: 10.3785/j.issn.1008-973X.2023.08.018
机械工程、能源工程     
无先验模型曲面的机器人打磨主动自适应在线轨迹预测方法
郭万金1,2,3(),赵伍端1,于苏扬1,赵立军2,4,曹雏清3,4
1. 长安大学 道路施工技术与装备教育部重点实验室，陕西 西安 710064
2. 哈尔滨工业大学 机器人技术与系统国家重点实验室，黑龙江 哈尔滨 150001
3. 芜湖哈特机器人产业技术研究院有限公司 博士后工作站，安徽 芜湖 241007
4. 长三角哈特机器人产业技术研究院，安徽 芜湖 241007
Active adaptive online trajectory prediction for robotic grinding on surface without prior model
Wan-jin GUO1,2,3(),Wu-duan ZHAO1,Su-yang YU1,Li-jun ZHAO2,4,Chu-qing CAO3,4
1. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang’an University, Xi’an 710064, China
2. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
3. Post-Doctoral Research Center, Wuhu HIT Robot Technology Research Institute Co. Ltd, Wuhu 241007, China
4. Yangtze River Delta HIT Robot Technology Research Institute, Wuhu 241007, China
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摘要：

为了解决工业机器人打磨的作业柔顺性不足问题，设计了机器人柔顺浮动打磨力控末端执行器. 为了解决无先验模型曲面工件的机器人打磨轨迹自适应性较差的问题，提出无先验模型曲面的机器人打磨主动自适应在线轨迹预测方法. 该方法根据打磨工具与工件的接触状态预测曲面法线方向，以有向进给平面与曲面切平面的截交线作为进给导向，自适应在线预测机器人打磨系统末端执行器的位姿，使末端执行器主轴轴线实时主动跟踪曲面法线，实现对无先验模型曲面工件的机器人打磨轨迹的主动自适应在线预测. 通过仿真分析和实验验证了所提出方法的有效性，最大综合位置预测误差与曲面法线跟踪误差分别为0.506 mm和4.912°. 所提出方法可以为无先验模型曲面工件的机器人打磨提供在线轨迹预测.
关键词： 工业机器人;  无先验模型曲面;  在线轨迹预测;  曲面法线跟踪;  机器人打磨    
Abstract:

A compliant floating grinding force-controlled end-effector was designed, in order to solve the problem of the insufficient compliance of industrial robot grinding. Also, an active adaptive online trajectory prediction method of robotic grinding for the workpiece surface without a prior model was proposed to address the poor adaptability problem in robotic grinding trajectory prediction for this kind of surface. The direction of the surface normal is predicted by the proposed method according to the contact state between the grinding tool and the workpiece, and the interception line of the directed feed plane and the surface tangent plane is used as the feed guide, the pose of the end-effector is obtained adaptively, so that the surface normal of the end-effector is actively tracked by the axis of the spindle in real time, and the active adaptive online trajectory prediction is implemented for the curved workpiece without a prior model. The effectiveness of the proposed method was verified by simulation analysis and robotic experiment. The maximum comprehensive position error and the surface normal tracking error were 0.506 mm and 4.912°, respectively. The proposed method can provide an online trajectory prediction method for robotic grinding on surface workpieces without the prior model.
Key words: industrial robot    surface without prior model    online trajectory prediction    surface normal tracking    robotic grinding
收稿日期: 2022-11-09 出版日期: 2023-08-31
CLC:  TP 242.2  
基金资助: 国家自然科学基金资助项目（52275005）；中央高校基本科研业务费专项资金资助项目(300102253201)；中国博士后科学基金资助项目（2022M722435）；哈尔滨工业大学机器人技术与系统国家重点实验室开放研究资助项目（SKLRS-2020-KF-08）；安徽省教育厅科学研究重点资助项目（KJ2020A0364）
作者简介: 郭万金（1983—），男，副教授，从事打磨工业机器人技术与自适应在线轨迹预测研究. orcid.org/0000-0001-9654-0113. E-mail： guowanjin@chd.edu.cn
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郭万金
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引用本文:

郭万金,赵伍端,于苏扬,赵立军,曹雏清. 无先验模型曲面的机器人打磨主动自适应在线轨迹预测方法[J]. 浙江大学学报(工学版), 2023, 57(8): 1655-1666.

Wan-jin GUO,Wu-duan ZHAO,Su-yang YU,Li-jun ZHAO,Chu-qing CAO. Active adaptive online trajectory prediction for robotic grinding on surface without prior model. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1655-1666.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.08.018    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I8/1655

图 1  柔顺浮动打磨力控末端执行器
图 2  机器人打磨系统虚拟样机
图 3  末端执行器位置控制系统和力控制器简化模型
图 4  基于力偏差的法向打磨力控制框图
图 5  主轴轴线方向与重力加速度方向夹角示意图
图 6  基于力偏差的法向打磨力控制框图（叠加重力补偿）
图 7  有向进给平面示意图
图 8  机器人打磨瞬时受力分析
图 9  无先验模型曲面的机器人打磨主动自适应在线轨迹预测方法控制框图
图 10  进给方向与曲面法线关系
图 11  工具进给过程
类别 位置预测 姿态预测（法线跟踪预测） 法向力跟踪
误差项 $ \Delta x $/mm $ \Delta y $/mm $ \Delta z $/mm $ \Delta d $/mm $ \Delta \alpha $/(°) $ \Delta \beta $/(°) $ \Delta \gamma $/(°) $ \Delta \delta $/(°) $\Delta {F_{\text{n} } ^{\max }}$/N $ {\Delta {\bar F_{\text{n}}}} $/N $\Delta {F_{\text{n} } ^{\rm{s} } }$
导纳控制 0.342 0.243 0.532 0.506 4.086 2.787 2.057×10?7 4.912 0.648 ?0.071 0.129
PID控制 0.128 0.248 0.422 0.677 4.058 2.767 5.136×10?8 4.946 0.905 0.044 0.024
表 1  自适应在线轨迹预测仿真结果
图 12  主动自适应在线轨迹预测的位置预测误差
图 13  在导纳控制下RPY角预测值及角度预测误差
图 14  在PID控制下RPY角预测值及角度预测误差
图 15  曲面法线跟踪误差
图 16  2种力控制器下跟踪正弦曲面的反作用力曲线
图 17  机器人打磨系统实验平台
图 18  圆台曲面铸铁工件
图 19  主动自适应在线轨迹预测实验
图 20  平台实验下的圆台曲面RPY角预测轨迹
图 21  平台实验下的圆台曲面位置预测轨迹
图 22  平台实验下的法向打磨力跟踪曲线
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