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Multisensory visual servoing by a neural network.

G Q Wei1, G Hirzinger

  • 1Siemens Corp. Res. Inc., Princeton, NJ.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces a neural network for robot motion determination, eliminating the need for sensor calibration. This approach effectively fuses camera and laser data for precise end-effector control without retraining.

Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Traditional robot motion determination relies on complex sensor calibration (camera, hand-eye), which is computationally intensive and challenging with diverse sensors.
  • Existing methods require recalibration for new tasks or sensor configurations, limiting adaptability.

Purpose of the Study:

  • To develop a calibration-free neural network approach for robot end-effector motion determination using multi-sensory data.
  • To enable direct transformation from sensory inputs to robot motions, simplifying the process and improving efficiency.
  • To achieve adaptability to changing goal positions without network retraining.

Main Methods:

  • A multilayer feedforward neural network was employed, taking camera images and laser range data as input.

Related Experiment Videos

  • A recursive motion strategy and network correction were used to relax the need for precise transformation learning.
  • Sensor fusion was achieved by integrating data from different sensor modalities into the neural network.

    • Main Results:

    • The proposed neural network approach successfully determined robot end-effector motion without requiring sensor or hand-eye calibration.
    • The method demonstrated effective sensor fusion of camera and laser data.
    • The system allowed for changes in goal positions without the need for retraining the neural network.

    Conclusions:

    • The calibration-free neural network approach offers a practical and efficient solution for robot motion determination.
    • This method simplifies robot system integration and enhances adaptability to dynamic environments and tasks.
    • The findings highlight the potential of neural networks for advanced sensor fusion and control in robotics.