Session: 06-10-01: Advanced Manufacturing in Aerospace Engineering

Paper Number: 166568

Proposal for a Smart OSAM Platform Leveraging 3D Scanning, Manufacturing, Robotics, and Edge Computing Technologies 

Abstract: On-orbit Servicing, Assembly, and Manufacturing (OSAM) has emerged as a key focus for space exploration and infrastructure development, attracting interest from government agencies, the military, and private industry. The ability to inspect, repair, and maintain spacecraft while in orbit is crucial for extending mission lifetimes, reducing costs, and enabling more complex space operations. This paper presents a novel approach to the in-orbit inspection and repair of defects on spacecraft exteriors. The proposed system utilizes a 3D laser scanner mounted on an articulated robotic arm to scan the exterior of a spacecraft. The scanner generates a high-resolution point cloud of the spacecraft hull, which is compared to a previously stored baseline scan of the undamaged spacecraft. Discrepancies between the two point clouds, representing defects, are automatically detected and localized through advanced comparison algorithms. This automated defect identification is crucial for precise, autonomous maintenance. Once a defect is identified, the data is transmitted to a central processing unit (CPU) via Robot Operating System 2 (ROS2). ROS2, while not a perfect representation of the communication protocols used in space, serves as a practical simulation for on-Earth applications, offering flexibility for multi-robot real-time communication. The CPU processes the defect data and uses a material property database along with intelligent topology optimization algorithms to design an efficient repair strategy. These algorithms compute the optimal distribution of repair material, ensuring minimal waste while restoring structural integrity. A path planning algorithm is then used to generate an optimal route for a small mobile robot, which is responsible for delivering the repair material to the defect site. For the proof-of-concept, the Dijkstra algorithm is employed to compute the shortest path while avoiding obstacles. The robot receives the computed coordinates through ROS2 and navigates to the defect location. Upon reaching the defect, the mobile robot uses an onboard localization system and fuzzy logic controller (FLC) to navigate the spacecraft surface accurately. Fuzzy logic control is particularly suitable for this application as it allows the robot to handle imprecise input, such as slight errors in localization or unexpected obstacles, ensuring smooth and adaptive navigation. Once positioned, the robot applies the required repair material, completing the task autonomously. This work demonstrates a comprehensive and scalable framework for autonomous in-orbit servicing, integrating 3D scanning, defect detection, path planning, and repair operations. The results show the potential for this system to address a wide range of spacecraft maintenance challenges. Future work will analyze different mobile robot architectures, use more adaptive localization and path-planning algorithms, and extend the platform’s capabilities to more complex repair scenarios.

Keywords: OSAM , mobile robots, spacecraft repair, edge computing, ROS2

Presenting Author: Ashton Orosa The University of Akron

Presenting Author Biography: Ashton Orosa is a graduate research assistant at University of Akron, specializing in robotics controls and applications. He holds a bachelor's degree in mechanical engineering from the University of Akron and has been actively engaged in research related to robotics in OSAM applications. Ashton has contributed to several projects on robotic design and control, OSAM, and fluid mechanics, with a particular interest in the intelligent control of robots. His recent work consists of the design of an OSAM platform. In addition to research, he has presented at the 2024 UCRO Symposium, where he discussed a testbed for the aforementioned OSAM platform. Currently, Ashton is focusing on the finalization of the OSAM/robotics platform project.

Authors:

Ashton Orosa The University of Akron
Kayla J. Dremann The University of Akron
Motaz Hassan The University of Akron
Xiaosheng Gao The University of Akron
Dane Quinn The University of Akron
Ajay Mahajan The University of Akron
Siamak Farhad The University of Tennessee