Hand-Eye Calibration and Inverse Kinematics of Robot Arm Using Neural Network

  • Haiyan Wu
  • Walter Tizzano
  • Thomas Timm Andersen
  • Nils Axel Andersen
  • Ole Ravn
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 274)


Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the artificial neural network inspired by the remarkable abilities of the animals in different tasks. This paper describes the theory and implementation of neural networks for hand-eye calibration and inverse kinematics of a six degrees of freedom robot arm equipped with a stereo vision system. The feedforward neural network and the network training with error propagation algorithm are applied. The proposed approaches are validated in experiments. The results indicate that the hand-eye calibration with simple neural network outperforms the conventional method. Meanwhile, the neural network exhibits a promising performance in solving inverse kinematics.


Neural Network Calibration Inverse Kinematics Robot Arm 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haiyan Wu
    • 1
  • Walter Tizzano
    • 1
  • Thomas Timm Andersen
    • 1
  • Nils Axel Andersen
    • 1
  • Ole Ravn
    • 1
  1. 1.Automation and Control, Department of Electrical EngineeringTechnical University of Denmark, ElektrovejKgs. LyngbyDenmark

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