AI-enabled Cyber-Physical In-Orbit Factory -- AI approaches based on digital twin technology for robotic small satellite production

TL;DR

The paper addresses the challenge of manufacturing and assembling small satellites directly in orbit to reduce launch costs and enable on-demand deployment. It proposes an AI-enabled cyber-physical in-orbit factory built around a Digital Process Twin that orchestrates robotic AIT, AI-based inspection, and adaptive planning, with teleoperation as a safe fallback. Core contributions include neural-network optical inspection, force-guided insertion with Q-learning for robust assembly, LOF-based electrical fault detection, adaptive teleoperation with multi-modal virtual fixtures, and a Digital Process Twin framework integrating AI for object recognition and state-driven planning. The results demonstrate a robotic AIT demonstrator, synthetic-data–driven training, and a cohesive architecture for autonomous in-orbit satellite production, highlighting significant potential for rapid, fault-tolerant space manufacturing and servicing. Future work targets fault detection, isolation, recovery, and rigorous qualification for space deployment, aiming to mature toward full in-orbit factory operation.

Abstract

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber-Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realisation of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail.

Figures (16)

• Figure 1: The AIT process in the "AI-In-Orbit-Factory" project
• Figure 2: AIT production environment with the YUMI robot for automating the integration process, optical inspection station (left) and electrical test setup (right)
• Figure 3: Overview image used to identify board type and orientation during optical inspection (left); microscope view of the indicated area (right), of a polluted board (top) and after cleaning (bottom).
• Figure 4: Microscope view of an undamaged (left) and damaged (right) satellite board: the trained neural network correctly classifies components (blue) and solderpads (green), as well as the visible defects (tombstone, orange) and pollution (solderball, pink) (lower row)
• Figure 5: Optical inspection of UNISEC connectors: missing, broken or bent connector pins are detected by measuring the deviation of their position from the ideal location relative to the connector casing.