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Deep neural network-based robotic visual servoing for satellite target tracking.

Shayan Ghiasvand1, Wen-Fang Xie1, Abolfazl Mohebbi2

  • 1Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, MontrĂ©al, QC, Canada.

Frontiers in Robotics and AI
|October 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep neural network (DNN) for automated satellite tracking on the ISS, reducing errors and costs. The DNN-based robotic visual servoing significantly improves tracking precision and efficiency.

Keywords:
deep learningdeep neural networkspose estimationrobot vision systemsvisual servoing

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Manual satellite tracking on the International Space Station (ISS) is costly and error-prone.
  • Current visual servoing methods struggle with motion decoupling, limiting tracking accuracy.

Purpose of the Study:

  • To develop a deep neural network (DNN)-based robotic visual servoing solution for automated satellite tracking.
  • To address and mitigate motion decoupling issues in visual servoing.
  • To enhance control performance and tracking precision for satellite operations.

Main Methods:

  • Utilized a deep neural network (DNN) to estimate manipulator pose.
  • Implemented a robotic visual servoing system with a 6-DOF Denso manipulator and an RGB camera.
  • Conducted real-time experimental tests using a targeting pin as a mock satellite.

Main Results:

  • Achieved a 32.04% reduction in pose error compared to conventional methods.
  • Demonstrated a 21.67% improvement in velocity precision.
  • Successfully reduced coupling effects through DNN-based pose estimation.

Conclusions:

  • The DNN-based robotic visual servoing method significantly enhances accuracy and efficiency in satellite tracking.
  • This approach offers a viable solution to the challenges of motion decoupling in visual servoing.
  • The findings indicate potential for improved satellite target tracking and capturing operations.