Parallel Six-Axis Force Sensor with Heavy-load Capacity and Static Calibration
CAI Da-jun1,3,YAO Jian-tao1,2,LI Ying-kang1,YI Wang-min4,XU Yun-dou1,2,ZHAO Yong-sheng1,2
1. Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Key Laboratory of Advanced Forging ＆ Stamping Technology and Science, Ministry of Education of China, Yanshan University, Qinhuangdao,Hebei 066004, China
3. Engineering Training Center of Yanshan University, Qinhuangdao, Hebei 066004, China
4.Beijing Institute of Spacecraft Environment Engineering, Beijing 100094
Abstract：Aim at the requirements of the sensor on heavy load and small size, a hybrid branch parallel six-axis force with havey-load capacity is proposed, the structural characteristics and measuring mechanism are also explained. The calibration system of six-axis force sensor is built, and in order to improve the effect of dimensional coupling and manufacturing error on the measurement accuracy of the sensor, the model optimization of calibration algorithm is studied. The least square method and BP neural network calibration algorithm are respectively used to calibration analyze the loading experimental data, the results show that the BP neural network algorithm is better than the least square method, and the correctness of analysis results is proved by the grouping test of random data. Based on the BP neural network, a BP neural network algorithm based on artificial fish swarm algorithm is proposed, and the calibration data is calculated and analyzed by using the optimized BP neural network algorithm, the results show that the BP neural network algorithm based on artificial fish swarm algorithm is more stable and difficult to fall into local extremum.
［1］Yoshikawa T, Miyazaki T. A six-axis force sensor with threedimensional cross-shape structure［C］//IEEE. IEEE International Conference on Robotics & Automation. 2002.
［2］Okumura D, Sakaino S, Tsuji T. Development of a multistage six-axis force sensor with a high dynamic range［C］//IEEE. IEEE International Symposium on Industrial Electronics. 2017.
［3］Gab-Soon K. The design of a six-component force/mom-ent sensor and evaluation of its uncertainty ［J］. Measurement Science and Technology, 2001, 12 (9): 1445-1455.
［4］王宣银, 尹瑞多. 基于Stewart机构的六维力/力矩传感器 ［J］. 机械工程学报, 2008, 44 (12): 118-122.
Wang X Y, Yin R D. Six-axis force/torque sensor based on Stewart platform ［J］. Journal of Mechanical Engi-neering, 2008, 44 (12): 118-122.
［5］张强, 宋爱国, 刘玉庆, 等. 一种指尖三维力传感器设计 ［J］. 计量学报, 2018, 39 (1): 52-55.
Zhang Q, Song A G, Liu Y Q, et al. Design of a three dimensional force sensor［J］. Acta Metrologica Sinica, 2018, 39 (1): 52-55.
［6］贾振元, 高翼飞, 任宗金, 等. 六维力压电天平研制与静态性能测试研究 ［J］. 大连理工大学学报, 2014, 54 (1): 43-48.
Jia Z Y, Gao Y F, Ren Z J, et al. Development and static performance measurement research for six-component piezoelectric balance ［J］. Journal of Dalian University of Technology, 2014, 54 (1): 43-48.
［7］付立悦, 宋爱国. 六维力传感器静态标定系统误差分析 ［J］. 计量学报, 2019, 40 (2): 295-299.
Fu L Y, Song A G. Error Analysis of Six-axis Force/Torque Sensors Static Calibration System ［J］. Acta Metrologica Sinica, 2019, 40 (2): 295-299.
［8］赵浩, 冯浩. 一种振动转矩传感器标定方法研究 ［J］. 计量学报, 2018, 39 (2): 178-181.
Zhao H, Feng H. Study on A Novel Calibration Method for Vibration Torque Sensor ［J］. Acta Metrologica Sinica, 2018, 39 (2): 178-181.
［9］张景柱, 郭凯, 徐诚. 六维力传感器静态解耦算法应用研究 ［J］. 传感器与微系统, 2007, 26 (12): 58-59.
Zhang J Z, Guo K, Xu C. Application study on static decoupling algorithms for six-dimensional force sensor ［J］. Transducer and Microsystem Technologies, 2007, 26 (12): 58-59.
［10］茅晨, 宋爱国, 高翔, 等. 六维力/力矩传感器静态解耦算法的研究与应用 ［J］. 传感技术学报, 2015, 28(2): 205-210.
Mao C, Song A G, Gao X, et al. Research and App-lication of Static Decoupling Algorithm for Six-axis Force/Torque Sensor ［J］. Chinese Journal of Sensors and Actuators, 2015, 28(2): 205-210.
［11］张家敏, 许德章. 改进粒子群优化BP神经网络的六维力传感器解耦研究 ［J］. 仪表技术与传感器, 2016, (7): 8-11.
Zhang J M, Xu D Z. BP Network Based Six-axis Force Sensor Decoupling Modified Particle Swarm Optimiz-ation ［J］. Instrument Technique and Sensor, 2016, (7): 8-11.
［12］王有贵, 吴双双, 陈红江. 称重传感器蠕变误差的神经网络补偿方法 ［J］. 计量学报, 2018, 39 (4): 510-514.
Wang Y G, Wu S S, Chen H J. Compensation Method for Creep Error of Load Cell Based on Neural Networks ［J］. Acta Metrologica Sinica, 2018, 39 (4): 510-514.
［13］姚建涛, 蔡大军, 朱佳龙, 等. 容错并联式六维力传感器可靠性及冗余分析 ［J］. 仪器仪表学报, 2015, 36 (8): 1699-1706.
Yao J T, Cai D J, Zhu J L, et al. Reliability and redundancy analysis of fault-tolerant parallel six-axis force sensor ［J］. Chinese Journal of Scientific Instrument, 2015, 36 (8): 1699-1706.
2021, Vol. 42 
