| 吴佳敏,王鹏,付麒瑞,孙茹雪,张昌明,杨帆,戴裕强.指尖六维力传感器标定平台与拟合方法研究[J].电子测量与仪器学报,2026,40(5):234-245 |
| 指尖六维力传感器标定平台与拟合方法研究 |
| Research on the calibration platform and fitting methods fora fingertip six-axis force sensor |
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| DOI： |
| 中文关键词: 6维力传感器 标定系统 独立加载装置 PSO-BP神经网络 支持向量回归（SVR） 非线性拟合方法 |
| 英文关键词:six-dimensional force sensor calibration system loading device PSO-BP SVR nonlinear fitting |
| 基金项目:陕西省重点研发计划(2024GX-YBXM-198) 、陕西省教育厅创新团队项目（24JP032）、陕西省秦创原四链融合项目（2024PT-ZCK-38）资助 |
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| Author | Institution |
| Wu Jiamin | School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China |
| Wang Peng | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China;
2.Shaanxi Provincial Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Fu Qirui | School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China |
| Sun Ruxue | School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China |
| Zhang Changming | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China;
2.Shaanxi Provincial Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Yang Fan | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China;
2.Shaanxi Provincial Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Dai Yuqiang | School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001,China |
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| 摘要点击次数: 239 |
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| 中文摘要: |
| 为提高6维力传感器标定过程中力/力矩加载不准确、多自由度组合加载的可实现性，通过六自由度独立加载静态标定装置，构建了基于力锤激励的动态标定系统，并通过模态分析获取传感器结构的1阶与3阶模态频率，评估其在多频激励下的动态响应特性，以避免共振干扰、提升动态响应准确性。为评估所设计标定装置性能，采集已知载荷与输出的6维力/力矩数据，分别采用传统线性拟合、粒子群优化的BP神经网络（particle swarm optimization-backpropagation neural network, PSO-BP）以及支持向量回归（support vector regression, SVR）对标定装置输出数据与加载载荷之间的映射关系进行建模与拟合，并对比各方法在6个通道的拟合效果。结果表明，PSO-BP神经网络在各通道中拟合精度最优，其平均绝对误差（mean absolute error, MAE）为17.86%，相较线性拟合降低8.20%，相较支持向量回归方法则降低幅度更大。该标定系统具备良好的加载精度和动态性能，验证了PSO-BP神经网络在多维力/力矩标定中的优越性与实用性。 |
| 英文摘要: |
| To improve the accuracy of force/torque loading and the feasibility of multi-degree-of-freedom combined loading during the calibration of six-axis force sensors, a dynamic calibration system based on impact-hammer excitation was developed by utilizing a six-degree-of-freedom independent loading static calibration apparatus. The first- and third-order modal frequencies of the sensor structure were obtained through modal analysis to evaluate its dynamic response under multi-frequency excitation, thereby avoiding resonance effects and improving dynamic calibration accuracy. To assess the performance of the proposed calibration system, six-dimensional force/torque data were collected under known loading conditions. Three modeling and fitting methods—traditional linear regression, particle swarm optimization-based backpropagation neural network (PSO-BP), and support vector regression (SVR)—were employed to establish the mapping relationship between the sensor outputs and the applied loads. The fitting performance of the three methods was compared across all six channels. The results indicate that the PSO-BP neural network achieved the highest fitting accuracy, with the mean absolute error (MAE) of 17.86%，which reduced by 8.20% compared to linear regression, and significantly more than the SVR method. The proposed calibration system demonstrates excellent static and dynamic performance, validating the superiority and practicality of the PSO-BP neural network in multi-dimensional force/torque sensor calibration. |
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