An Efficient Genetic Algorithm for Active Space Debris Removal Planning

Abstract

As the number of debris increases, the space environment becomes crowded and the risk of collision increases. The active debris removal (ADR) technology is an effective measure to suppress the growth of space debris and stabilize the space environment at a safe level. The ADR based a parent spacecraft carrying multi-sub-satellite is one of the most popular methods to de-orbit space debris. The sub-satellites are equipped with different de-orbit devices to capture the debris and move them to lower orbits. One of the key issues have to deal with is that how to plan an efficient strategy for these sub-satellites to visit all the identified debris in one mission at the lowest weighted sum of energy and time. In order to solve the problem, we model the multi-sub-satellite path planning into a multiple traveler salesmen problem (MTSP). Moreover, we propose an improved multi-sub-population parallel genetic algorithm named Route-Break chromosome pairing, it uses an integer coding method and a new crossover-mutation strategy called single chromosome transformation. Each sub-population evolves independently. Finally, we carry out experiments to testify the efficiency of the proposed algorithm. From the experimental results, we can see that the proposed method has a fast convergence speed and a better solution.