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Monocular Visual Measurement System Uncertainty Analysis and One-Step End-End Estimation Upgrade.

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  • 1School of Aeronautical Engineering, Nanjing University of Industry Technology, Nanjing 210023, China.

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Summary
This summary is machine-generated.

This study introduces an uncertainty analysis for monocular vision systems in automated manufacturing, leading to a novel one-step method for robot and hand-eye calibration. This approach enhances assembly accuracy for vision-guided robotics.

Keywords:
aircraft assemblymonocular visionone-step end-to-endpose estimationuncertainty analysis

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Area of Science:

  • Robotics
  • Computer Vision
  • Manufacturing Engineering

Background:

  • Monocular visual measurement and vision-guided robotics are crucial in automated manufacturing, especially aerospace assembly.
  • Multi-source error accumulation from visual measurement, hand-eye calibration, and robot calibration degrades final assembly accuracy.

Purpose of the Study:

  • To develop an uncertainty analysis method for monocular visual measurement systems in assembly pose estimation.
  • To propose a direct, one-step solution for robot and hand-eye calibration problems informed by uncertainty analysis.
  • To construct a high-performance, end-to-end pose estimation convolutional neural network (OECNN).

Main Methods:

  • Developed an uncertainty analysis method for monocular visual measurement systems, detailing uncertainty propagation paths and input values.
  • Applied nonlinear mapping estimation for a direct, one-step solution to robot and hand-eye calibration challenges.
  • Constructed and validated the one-step end-to-end pose estimation convolutional neural network (OECNN).

Main Results:

  • The OECNN directly maps target object pose variation to positioner drive volume variation.
  • Uncertainty analysis provided key insights for improving assembly pose estimation precision.
  • Experimental validation confirmed the high accuracy and applicability of the proposed one-step method.

Conclusions:

  • The proposed uncertainty analysis methodology offers a valuable reference for complex system uncertainty analysis.
  • The one-step end-to-step pose estimation method significantly enhances accuracy in automated assembly tasks.
  • The approach is particularly suitable for high-precision applications like aircraft assembly using vision-guided robots.