An Interpolated Approach for Active Debris Removal

Abstract

The continuous use of satellite networks in the Low Earth Orbit (LEO) has accumulated a large amount of space debris. Given the actual state of the orbit, these debris are a threat to the active systems and to the feasibility of future operations in LEO. Now, Active Debris Removal (ADR) missions must be conducted to mitigate the debris through forced deorbitation. The best documented approaches for the ADR mission planning made use of metaheuristics, modeling the ADR as a complex variant of the TSP. However, these approaches usually fail to deal some of the ADR problem dynamics, such as large instances, mission constraints or the debris motion. In this paper we propose heuristic of continuous improvement on a genetic-based solution. Our work advances the state of the art by dealing with large real world instances, modeling all the constraints and considering the problem time dependence (motion). Experiments were conducted to evidence the improvements over the literature. With the ability of generating time-dependent results for scenarios with thousands of debris in a feasible time, our approach yielded missions 96.33 % more effective at the cleaning job than the present ones on the literature.