Ant colony optimization-based design of multiple-target active debris removal mission

Abstract

The paper considers the ant colony optimization (ACO) methodology for designing an active debris removal mission. The goal is to optimize a sequence of transfers using an orbital transfer vehicle to rendezvous with multiple pieces of debris for the purpose of removal. The methodology consists of two phases: the first phase obtains an optimal removal sequence, and the second phase is related to transfer trajectory optimization. During the sequence planning process, a refined approximation is proposed to estimate the transfer times and necessary costs of individual transfers. The problem can then be mapped into a variant of the traveling salesman problem (TSP). To solve it, an enhanced ACO and the inver-over algorithm are proposed. The effectiveness of the ACO heuristic is proved over a set of instances with different sizes ranging from 100 to 2000. In the second phase, each transfer leg in the optimal sequence is verified using the continuous ACO proposed. The computational results show that the methodology proposed can optimally select targets from a debris archive of considerable size (i.e., up to 2000 debris pieces). Additionally, the mitigation of 13–20 objects, with total "V below 1 km/s, is feasible in less than a year.