Autonomous Logistics and Inventory Management in a Modular, Robotic, and Extensible Space Station

Human-crewed space stations have played a pivotal role in space exploration since the early 1980s, with notable examples including the Russian Mir and the International Space Station (ISS). While the ISS is currently the sole functioning manned space station, the landscape of space exploration is evolving with the emergence of private space companies like SpaceX and Blue Origin. This shift has sparked growing interest in the development of private space stations for both commercial and research purposes, offering potential cost-effectiveness and flexibility in space endeavors. However, the challenges posed by space stations, particularly for human-crewed missions, are considerable. The need for human crews escalates mission complexity and costs significantly. Systems like the Environmental Control and Life Support System (ECLSS) within the ISS represent substantial investments in terms of cost, mass, volume, and maintenance. Furthermore, prolonged exposure to microgravity and radiation in space exerts adverse effects on human health. The supply chain and logistics for sustaining human-crewed space stations are intricate, and this complexity intensifies as missions move beyond Low Earth Orbit (LEO) to explore deep space. Uncrewed space stations, on the other hand, offer various advantages. They present a cost-effective alternative to manned missions and can operate autonomously, making them suitable for deep-space endeavors. This autonomy reduces construction and maintenance costs while enabling extended missions without the need for crew rotation or resupply. Uncrewed stations excel in data collection and transmission from space, capturing valuable insights into celestial bodies, the universe’s composition, and the potential existence of extraterrestrial life. These stations can facilitate long-term scientific experiments, overcoming the limitations imposed by human crews. They also serve as testing platforms for innovative space technologies, contributing to the advancement of space exploration. Despite their advantages, uncrewed space stations face challenges in inventory management. Autonomous robotic inventory management has been demonstrated in previous space missions, such as NASA’s Robonaut 2 and the Synchronized Position Hold, Engage, Reorient Experimental Satellites (SPHERES) on the ISS. These robots scan, identify, and relocate items within the station, optimizing inventory management processes. Future missions, like NASA’s Mars Sample Return, plan to utilize autonomous robotic inventory management for efficient sample storage and transport. As robotics technology and machine learning advance, robots become increasingly capable of autonomous tasks even in harsh space environments, revolutionizing inventory management in space stations. This paper delves into the development of a comprehensive decentralized logistics planner for a Modular, Extensible, Autonomous, Robotic space station situated in CisLunar Space. The station’s primary role is serving as an observation post equipped with a variety of imaging sensors and spectrometers. These instrument modules, in CubeSat form, function independently but can be configured into task- specific autonomous setups, enhancing mission-specific data collection. However, the presence of multiple entities in an autonomous system necessitates continuous tracking and monitoring. Damaged or underperforming modules require servicing or replacement, calling for onboard warehousing. These small modules can be launched as secondary payloads, optimizing space in larger launch vehicles. The paper outlines a detailed system architecture for various operational phases and explores nominal and off-nominal conditions, including disaster management, assessing the reliability and robustness of the planner.