The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We present a new technique for multi-axis force/torque sensor calibration called shape from motion. This technique retains the noise rejection of a highly redundant data set but eliminates the need for explicit knowledge of the redundant applied load vectors, yielding faster, more accurate calibration results. A constant-magnitude force (a mass in a gravity field) is randomly moved through the sensing space while raw data is continuously gathered. Using only the raw sensor signals, the motion of the force vector (the "motion") and the calibration matrix (the "shape") are simultaneously extracted by singular value decomposition. We have applied this technique to several types of force/torque sensors and present experimental results for a 2-DOF fingertip and a 6-DOF wrist sensor with comparisons to the standard least squares approach.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Richard Voyles, James Morrow, and Pradeep Khosla, "Shape from Motion Approach to Rapid and Precise Force/Torque Sensor Calibration," ASME International Mechanical Engineering Congress and Exposition, November, 1995.

BibTeX Reference

@inproceedings{Voyles_1995_1793,
   author = "Richard Voyles and James Morrow and Pradeep Khosla",
   title = "Shape from Motion Approach to Rapid and Precise Force/Torque Sensor Calibration",
   booktitle = "ASME International Mechanical Engineering Congress and Exposition",
   month = "November",
   year = "1995",
}

The Robotics Institute

Menu