Optimization and Modeling of Active Debris Removal using a Time-Expanded Network

View Video Presentation: https://doi.org/10.2514/6.2022-4245.vid This paper develops a framework to optimize an active debris removal (ADR) mission utilizing a time-expanded network. Given the challenging and costly nature of active debris removal, it is desirable to minimize the cost of the mission while maximizing the number of debris collected to determine the most effective and efficient concept of operations. Solving a traveling salesman problem with variable costs due to the dynamic nature of space debris is a challenging problem that is simplified and aided by the use of a time-expanded network. Using this formulation, an optimal mission scenario can efficiently and accurately be computed from a large set of potential debris to be removed. A case study demonstrates the usefulness of this approach, examining a mission using a high-thrust engine to stabilize the debris environment.