会议论文详细信息
2017 International Conference on Sensors, Materials and Manufacturing | |
An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor | |
工业技术;材料科学;机械制造 | |
Fu, Liyue^1 ; Song, Aiguo^1 | |
School of Instrument Science and Engineering, Southeast University, Nanjing, China^1 | |
关键词: 6-axis force/torque sensors; BP neural networks; Decoupling algorithms; Decoupling results; GA-BP algorithms; Measurement precision; Pseudo inverse matrix; Six-axis force/torque sensor; | |
Others : https://iopscience.iop.org/article/10.1088/1757-899X/311/1/012002/pdf DOI : 10.1088/1757-899X/311/1/012002 |
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来源: IOP | |
【 摘 要 】
In order to improve the measurement precision of 6-Axis force/torque sensor for robot, BP decoupling algorithm optimized by GA (GA-BP algorithm) is proposed in this paper. The weights and thresholds of a BP neural network with 6-10-6 topology are optimized by GA to develop decouple a six-Axis force/torque sensor. By comparison with other traditional decoupling algorithm, calculating the pseudo-inverse matrix of calibration and classical BP algorithm, the decoupling results validate the good decoupling performance of GA-BP algorithm and the coupling errors are reduced.
【 预 览 】
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An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor | 1032KB | download |