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

TL;DR

This paper introduces the MMEDR-Autonomous framework, integrating machine learning-based navigation and guidance with a hardware-in-the-loop testbed to enhance autonomous rendezvous and docking for space applications.

Contribution

It presents a unified framework combining optical navigation, reinforcement learning guidance, and safety constraints for multi-agent space rendezvous, with ongoing experimental validation.

Findings

Lightweight monocular pose estimation network trained on realistic augmentations.

Analysis of learning stability and hyperparameter tuning for guidance.

Review of Control Barrier Function results for safety enforcement.

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.