A new robot finger force sensor using neural network

Kuribayashi, Katsutoshi; Shimizu, Seiji; Yuzawa, Tomoyuki; Taniguchi, Takao

doi:10.1109/iros.1993.583101

Cited by 3 publications

(1 citation statement)

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“…BP neural network, a feed-forward multi-layer network based on back propagation algorithm, has become one of the most widely used artificial neural networks (ANN) in practice [5] [7]. BP was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions.…”

Section: A the Structure Determination Of Bp Networkmentioning

confidence: 99%

Application of Neural Network to Nonlinear Static Decoupling of Robot Wrist Force Sensor

Lei

Qiu

Liu

et al. 2006

2006 6th World Congress on Intelligent Control and Automation

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The static coupling of wrist force sensor is a major influencing factor to its measuring precision. Aiming at resolving the disadvantages such as low decoupling precision of the traditional method, we put forward a nonlinear decoupling method based on neural network. The major idea applied is to use the BP network to realize the mapping from input to output of the sensor. Owing to BP network's good nonlinear mapping ability, the decoupling result can reach an arbitrary precision theoretically. The effectiveness of this method was verified in the calibration of wrist force sensor of a force sensing system for an underwater robot griper. The decoupling results demonstrate the validation of neural network method.Index Terms -wrist force sensor; static decoupling; underwater robot gripper; neural network.

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Section: A the Structure Determination Of Bp Networkmentioning

confidence: 99%