Target selection algorithm for multiple-capture orbital debris remediation spacecraft missions
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Authors
Hudnut, William Otis
Issue Date
2021-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Aeronautical engineering
Alternative Title
Abstract
One major obstacle to successful orbital debris remediation is the determination of which pieces of debris are the most viable targets for capture and de-orbit. The viability of a target is determined by some combination of the debris’ risk factor (a combination of its size, composition, and the orbit it occupies), the anticipated resource cost to find and capture the debris, and the underlying probability of successful intercept and capture of that target. The problem of selecting debris for capture by a multi-capture capable spacecraft is fundamentally a traveling salesman problem in which the traveler only has the resources to reach a very limited subset of the available destinations. This problem must be solved, however, since any debris removal spacecraft must be launched into orbit itself, likely producing some debris in its launch. In order to effectively reduce orbital debris populations, space-based solutions must be capable of multiple de-orbits per mission. The solution presented here uses iterative filtering and sorting of existing debris databases to produce lists of up to four targets (the maximum which can be achieved by the OSCaR debris capture CubeSat) which are feasible given the initial conditions of the spacecraft after its launch. A non-Hohmann two-burn node-to-node transfer calculation method was chosen, as it is very close to the energy-optimal transfer between two near-circular inclined orbits. This method is capable of capturing all valid target sets from target lists produced by Hudnut, Mehlman, and Anderson's database filter-sorter algorithm [1], with a run time of approximately 13 seconds on a laptop computer.
Description
May 2021
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY