基于轨迹优化和主成分分析的1T2R并联动力头误差辨识与补偿

IF 1.9 4区 计算机科学 Q3 ENGINEERING, INDUSTRIAL Industrial Robot-The International Journal of Robotics Research and Application Pub Date : 2023-03-03 DOI:10.1108/ir-09-2022-0234
Y. Ni, Yizhang Cui, Shi Jia, Chenghao Lu, Wen-jin Lu
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引用次数: 0

摘要

目的提出一种选择误差测量位置和姿态轨迹的方法,以提高一平移两转动并联动力头的运动标定效率,并通过改进误差辨识矩阵的性质来提高误差补偿效果。设计/方法/方法首先,建立了端点综合误差与各几何误差源的一般映射模型;其次,提出了基于灵敏度分析结果的误差测量位置和姿态轨迹优化模型,为优化机构在工作空间中的误差测量轨迹提供了依据。最后,利用距离误差测量信息和主成分分析(PCA)思想构建误差识别矩阵。通过仿真和实验验证了该辨识算法的鲁棒性和补偿效果。通过灵敏度分析发现,各误差源的灵敏度系数在工作空间平面上的分布可以近似地表示其在工作空间中的分布,当机构末端以较大的章动角作圆周运动时,各灵敏度的综合影响系数最大。残差分析表明,采用主成分分析思想的识别算法的鲁棒性得到了提高。通过实验,发现补偿效果得到了改善。提出了误差测量位置和姿态轨迹优化模型,可有效提高1T2R并联机构的误差测量效率。此外,还介绍了主成分分析的思想。提出了一种最小二乘PCA误差识别算法,通过改善识别矩阵的性质来提高识别算法的鲁棒性,并提高了补偿效果。该方法已通过1T2R并联机构的实验验证,可推广应用于其他类似并联机构。
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Error identification and compensation of 1T2R parallel power head based on trajectory optimization and principal component analysis
Purpose The purpose of this paper is to propose a method for selecting the position and attitude trajectory of error measurement to improve the kinematic calibration efficiency of a one translational and two rotational (1T2R) parallel power head and to improve the error compensation effect by improving the properties of the error identification matrix. Design/methodology/approach First, a general mapping model between the endpoint synthesis error is established and each geometric error source. Second, a model for optimizing the position and attitude trajectory of error measurement based on sensitivity analysis results is proposed, providing a basis for optimizing the error measurement trajectory of the mechanism in the working space. Finally, distance error measurement information and principal component analysis (PCA) ideas are used to construct an error identification matrix. The robustness and compensation effect of the identification algorithm were verified by simulation and through experiments. Findings Through sensitivity analysis, it is found that the distribution of the sensitivity coefficient of each error source in the plane of the workspace can approximately represent its distribution in the workspace, and when the end of the mechanism moves in a circle with a large nutation angle, the comprehensive influence coefficient of each sensitivity is the largest. Residual analysis shows that the robustness of the identification algorithm with the idea of PCA is improved. Through experiments, it is found that the compensation effect is improved. Originality/value A model for optimizing the position and attitude trajectory of error measurement is proposed, which can effectively improve the error measurement efficiency of the 1T2R parallel mechanism. In addition, the PCA idea is introduced. A least-squares PCA error identification algorithm that improves the robustness of the identification algorithm by improving the property of the identification matrix is proposed, and the compensation effect is improved. This method has been verified by experiments on 1T2R parallel mechanism and can be extended to other similar parallel mechanisms.
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来源期刊
CiteScore
4.50
自引率
16.70%
发文量
86
审稿时长
5.7 months
期刊介绍: Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world. The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to: Automatic assembly Flexible manufacturing Programming optimisation Simulation and offline programming Service robots Autonomous robots Swarm intelligence Humanoid robots Prosthetics and exoskeletons Machine intelligence Military robots Underwater and aerial robots Cooperative robots Flexible grippers and tactile sensing Robot vision Teleoperation Mobile robots Search and rescue robots Robot welding Collision avoidance Robotic machining Surgical robots Call for Papers 2020 AI for Autonomous Unmanned Systems Agricultural Robot Brain-Computer Interfaces for Human-Robot Interaction Cooperative Robots Robots for Environmental Monitoring Rehabilitation Robots Wearable Robotics/Exoskeletons.
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