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Strategies for Improving and Evaluating Robot Registration Performance.

Karl Van Wyk1, Jeremy A Marvel1

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Improving robot registration accuracy is key for efficient robotics. This study shows that using more data and machine learning significantly reduces errors, leading to better task performance in peg-in-hole tests.

Keywords:
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Area of Science:

  • Robotics
  • Computer Vision
  • Mechanical Engineering

Background:

  • Robot registration, calculating transformations between coordinate systems, is crucial for robotic tasks.
  • Dynamic workcells require robots to be programmed in a unifying reference frame for collaboration and integration.
  • Existing registration methods need to be inexpensive, fast, and accurate.

Purpose of the Study:

  • To investigate strategies for improving robot registration accuracy between a robotic arm and an extrinsic coordinate system.
  • To expand a previous framework with a new test methodology for evaluating registration performance.
  • To demonstrate application-level performance gains from improved registration accuracy.

Main Methods:

  • Developing and expanding a framework for robot registration.
  • Implementing a new test methodology to characterize strategies for improving registration performance.
  • Conducting statistical analyses of physical trials and one-shot peg-in-hole tests.

Main Results:

  • Leveraging more data and applying machine learning significantly reduce registration error.
  • Improved registration accuracy leads to enhanced performance in peg-in-hole insertion tasks.
  • Maximum translation positioning error post-registration is a strong indicator of peg insertion success.

Conclusions:

  • Strategies incorporating more data and machine learning offer significant improvements in robot registration accuracy.
  • Accurate robot registration directly translates to more reliable task-level performance.
  • The developed methods provide a quantitative verification process for registration strategies.