Active adaptive online trajectory prediction for robotic grinding on surface without prior model

doi:10.3785/j.issn.1008-973X.2023.08.018

Journal of ZheJiang University (Engineering Science)

2023, Vol. 57

Issue (8): 1655-1666 DOI: 10.3785/j.issn.1008-973X.2023.08.018

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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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

Received: 09 November 2022 Published: 31 August 2023

CLC:

TP 242.2

Fund: 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）

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	Wan-jin GUO
	Wu-duan ZHAO
	Su-yang YU
	Li-jun ZHAO
	Chu-qing CAO

Cite this article:

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.

URL:

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

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

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

Fig.1 Compliant floating grinding force-controlled end-effector

Fig.2 Virtual prototype of robotic grinding system

Fig.3 Simplified model of position control system and force controller for end-effector

Fig.4 Block diagram of normal grinding force control based on force error

Fig.5 Schematic diagram of angle between axis of spindle and gravity vector

Fig.6 Block diagram of normal grinding force control based on force error (with gravity compensation)

Fig.7 Schematic diagram of directional feed plane

Fig.8 Instantaneous force analysis of robotic grinding

Fig.9 Block diagram of active adaptive online trajectory prediction method of robotic grinding on surface without prior model

Fig.10 Relationship between feed direction and surface normal

Fig.11 Schematic diagram of feeding process

Tab.1 Simulation results of adaptive online trajectory prediction

Fig.12 Position prediction error of active adaptive online trajectory prediction

Fig.13 RPY angle prediction and angle prediction error with admittance controller

Fig.14 RPY angle prediction and angle prediction error with PID controller

Fig.15 Surface normal tracking error

Fig.16 Reaction force tracking curve of a sinusoidal surface with two different force controllers

Fig.17 Experimental platform of robotic grinding system

Fig.18 Cast iron workpiece with circular frustum surface

Fig.19 Experiment of active adaptive online trajectory prediction

Fig.20 RPY angle prediction for circular frustum surface on platform experiment

Fig.21 Position prediction for circular frustum surface on platform experiment

Fig.22 Normal grinding force tracking curve on platform experiment


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