Autonomous robotic system inspects large space structures without markers
Markerless Multi-Modal Autonomous Robotic Inspection of Large Space Structures
Robotics
Summary
Inspecting big space structures like antennas or solar panels is hard because they can be huge and don’t have special markers to guide robots. The authors built a robot system that uses cameras and sensors to create 3D maps and thermal images without needing these markers. It plans safe paths to move around and collects detailed data about the structure. The system was tested in simulations and labs, showing it can work on big, unknown space objects without prior models.
What this means in practice
- •For orbital satellite operators: Inspect large satellites and space stations autonomously without needing prior models or markers, improving safety and efficiency of maintenance.
- •For industrial inspection teams: Use markerless multimodal 3D reconstruction techniques for inspecting large, complex terrestrial structures where deploying markers is impractical.
Authors
Juan De Dios Alfaro, Arturo Ríos, David Rodríguez-Martínez, Carlos Pérez-del-Pulgar
Abstract
Future orbital infrastructures, such as deployable antennas, solar farms, and large orbital platforms will require autonomous inspection systems able to operate with limited prior knowledge and without cooperative markers. Current on-orbit servicing approaches often rely on predefined trajectories, standard interfaces, fiducial markers or accurate target models, which limits scalability for large, heterogeneous or partially unknown structures. This paper presents a markerless autonomous robotic inspection pipeline in which 3D reconstruction is used as an inspection-support representation. The system integrates a Kinova Gen2 manipulator with an end-effector-mounted multimodal sensor head composed of an RGB-D camera, a thermal camera and a 2D LiDAR. The pipeline estimates an approximate inspection volume, generates viewpoints, plans collision-free motions with MoveIt, and synchronously records RGB-D images, thermal data, and robot poses in ROS2. Candidate reconstruction methods were evaluated to select a practical method for this pipeline, with Nerfacto used for geometric reconstruction and Thermal-Nerfacto used to demonstrate thermal-aware rendering for inspection. Validation in a Gazebo-based simulator and preliminary laboratory tests reveal that the proposed system can autonomously acquire spatially coherent inspection data and produce reconstructions suitable for visual and geometric assessment, representing a step towards inspection of large non-cooperative space structures.