Collection-as-a-Service to Incentivize On-Orbit Servicing in LEO

The space industry is consistently at the forefront of technology, inspiring generations and improving human understanding. The very industry that put people on the moon and gazes into the depths of space could also be one of the first industries to embrace circular economies. Low Earth Orbit (LEO) is home to an increasing number of mega-constellations, increasing orbital congestion amidst the worsening state of orbital debris. De-orbiting satellites at the end of their life is presently the best practice, but scientists are starting to recognize the environmental toll that de-orbiting satellites may take on the upper atmosphere. A sustainable space economy, featuring refuel depots and in-orbit servicers, would not only reduce the atmospheric pollution associated with de-orbiting satellites, but also expand our capacity for future space exploration as we improve our use of resources in a resource-starved environment. Space debris is a known problem and atmospheric pollution is a potential problem; improving the case for In-Space Servicing, Assembly, and Manufacturing (ISAM) in LEO could mitigate both. Despite the need for improved sustainability in LEO, there is little interest in ISAM for LEO. ISAM in geosynchronous orbit (GEO) is currently more attractive since GEO satellites are more expensive, have longer life cycles, and have very long orbital decay rates. ISAM in LEO would require several modifications due to inefficient maneuvering and the nature of profilerated customer constellations. There are several ISAM CONOPs and infrastructure elements that may improve the case for ISAM in LEO and ultimately promote space sustainability. This paper introduces a novel concept for collection-as-a-service (CAAS) combined with multi-echelon sparing, which could provide the economies of scale and flexibility necessary to jump-start LEO-based ISAM. To assess the viability of CAAS, this paper develops a flexibility framework that utilizes a discrete event simulation with customer and service-provider decision rules that respond to scenarios with various sources of uncertainty.