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Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation.

Zhou Hao1, R B Ashith Shyam1, Arunkumar Rathinam1

  • 1Surrey Space Center, University of Surrey, Guildford, United Kingdom.

Frontiers in Robotics and AI
|June 18, 2021
PubMed
Summary

This study introduces an intelligent visual Guidance, Navigation, and Control (GNC) system using Artificial Intelligence (AI) for on-orbit manipulation. The AI-enhanced GNC enables autonomous decision-making for complex space missions, moving towards fully autonomous orbital robotics.

Keywords:
Guidance Navigation and Controlartificial intelligenton-orbit servicepose estimationspace manipulatorspace robotics

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Area of Science:

  • Spacecraft Guidance, Navigation, and Control (GNC)
  • Artificial Intelligence (AI) in Robotics
  • On-Orbit Servicing and Debris Removal

Background:

  • Conventional GNC systems rely heavily on ground control, limiting onboard autonomy.
  • On-orbit manipulation tasks, like debris removal, present challenges for traditional GNC due to unpredictable scenarios.
  • Existing GNC architectures struggle with real-time decision-making for complex, unmodelled situations.

Purpose of the Study:

  • To develop and demonstrate a novel intelligent visual GNC system for on-orbit manipulation tasks.
  • To integrate state-of-the-art AI modules into a conventional GNC framework as an intermediate solution.
  • To enable real-time decision-making and autonomous operations for spacecraft performing robotic tasks.

Main Methods:

  • Implemented a Deep Learning (DL)-based pose estimation algorithm for target pose determination from 2D images.
  • Utilized probabilistic modeling for space robot manipulator trajectory control, adaptable to unseen situations.
  • Integrated a 7-degrees-of-freedom robotic arm's trajectory learning module into the GNC system, using a centralized camera network.
  • Simulated the system using Matlab/Simulink and realistic physics rendering with Eevee for a conceptual mission (AISAT).

Main Results:

  • The DL pose estimation successfully determined target pose without prior target information.
  • The probabilistic trajectory control adapted to novel situations, minimizing spacecraft attitude disturbances.
  • Simulations of the AISAT mission demonstrated the intelligent GNC system's functionality for non-cooperative CubeSat manipulation.
  • Developed testbeds validated the GNC system's performance in realistic scenarios.

Conclusions:

  • The novel intelligent visual GNC system offers a viable intermediate solution towards fully autonomous orbital robotic systems.
  • AI integration enhances GNC capabilities for complex on-orbit servicing and manipulation tasks.
  • This approach paves the way for more autonomous and adaptable spacecraft operations in space.