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Current state of the multi-agent multi-view experimental and digital twin rendezvous (MMEDR-Autonomous) framework

Logan Banker, Michael Wozniak, Mohanad Alameer, Smriti Nandan Paul, David Meisinger, Grant Baer, Trevor Hunting, Ryan Dunham, Jay Kamdar

Abstract

As near-Earth resident space objects proliferate, there is an increasing demand for reliable technologies in applications of on-orbit servicing, debris removal, and orbit modification. Rendezvous and docking are critical mission phases for such applications and can benefit from greater autonomy to reduce operational complexity and human workload. Machine learning-based methods can be integrated within the guidance, navigation, and control (GNC) architecture to design a robust rendezvous and docking framework. In this work, the Multi-Agent Multi-View Experimental and Digital Twin Rendezvous (MMEDR-Autonomous) is introduced as a unified framework comprising a learning-based optical navigation network, a reinforcement learning-based guidance approach under ongoing development, and a hardware-in-the-loop testbed. Navigation employs a lightweight monocular pose estimation network with multi-scale feature fusion, trained on realistic image augmentations to mitigate domain shift. The guidance component is examined with emphasis on learning stability, reward design, and systematic hyperparameter tuning under mission-relevant constraints. Prior Control Barrier Function results for Clohessy-Wiltshire dynamics are reviewed as a basis for enforcing safety and operational constraints and for guiding future nonlinear controller design within the MMEDR-Autonomous framework. The MMEDR-Autonomous framework is currently progressing toward integrated experimental validation in multi-agent rendezvous scenarios.

Current state of the multi-agent multi-view experimental and digital twin rendezvous (MMEDR-Autonomous) framework

Abstract

As near-Earth resident space objects proliferate, there is an increasing demand for reliable technologies in applications of on-orbit servicing, debris removal, and orbit modification. Rendezvous and docking are critical mission phases for such applications and can benefit from greater autonomy to reduce operational complexity and human workload. Machine learning-based methods can be integrated within the guidance, navigation, and control (GNC) architecture to design a robust rendezvous and docking framework. In this work, the Multi-Agent Multi-View Experimental and Digital Twin Rendezvous (MMEDR-Autonomous) is introduced as a unified framework comprising a learning-based optical navigation network, a reinforcement learning-based guidance approach under ongoing development, and a hardware-in-the-loop testbed. Navigation employs a lightweight monocular pose estimation network with multi-scale feature fusion, trained on realistic image augmentations to mitigate domain shift. The guidance component is examined with emphasis on learning stability, reward design, and systematic hyperparameter tuning under mission-relevant constraints. Prior Control Barrier Function results for Clohessy-Wiltshire dynamics are reviewed as a basis for enforcing safety and operational constraints and for guiding future nonlinear controller design within the MMEDR-Autonomous framework. The MMEDR-Autonomous framework is currently progressing toward integrated experimental validation in multi-agent rendezvous scenarios.
Paper Structure (40 sections, 73 equations, 9 figures, 4 tables, 1 algorithm)

This paper contains 40 sections, 73 equations, 9 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Guidance
  4. Numerical Optimization
  5. Reinforcement Learning
  6. Control
  7. Constraints
  8. Clohessy-Wiltshire Control Barrier Functions
  9. Navigation
  10. Feature Extraction Algorithms
  11. Convolutional Neural Networks
  12. Training Data Generation
  13. Bridging the Space Domain Gap
  14. Networks in Rendezvous
  15. Hardware-in-the-Loop
  16. ...and 25 more sections

Figures (9)

  • Figure 1: Chaser and Target Spacecraft in LVLH Frame, Figure Adapted from khourythesis2020
  • Figure 2: Overall CNN Architecture for Optical Navigation, Based on Combination of MobileNetV3Large, FPN, and Deep-6DPose MobileNetV3FPNDeep-6DPose
  • Figure 3: Current State of the DCV-Space Testing Facility
  • Figure 4: CR20A Link Dimensions
  • Figure 5: Overview of All MMEDR Subsystems
  • ...and 4 more figures