A multi-objective planning method for multi-debris active removal mission in LEO

The multi-objective optimization formulation and a modified genetic algorithm (GA) based algorithm for mission planning of a multi-nanosatellite active debris removal platform (MnADRP) are introduced. MnADRP is a hybrid space vehicle consisting of one mother spacecraft carrying multiple nanosatellites. MnADRP performs multiple debris removal by sequentially transferring to the vicinity of a space debris in Low Earth Orbit (LEO) and releases the nanosatellites for removal operations. The goal of this work is to derive a joint debris selection and removal scheduling method for multiple debris removal missions with MnADRP that maximizes the aggregate benefit of removing selected debris, while (i) minimizing the energy to transfer, (ii) minimizing the consumption of nanosatellite payloads, and (iii) satisfying the total time duration and budget constraints of the mission. A multi-dimensional Pareto frontal surface criterion is incorporated in the fitness function to evaluate solutions generated by the genetic algorithm more efficiently. Based on real-world model parameters, extensive numerical simulations are conducted to evaluate the proposed debris selection and removal scheduling method. Compared to the conventional NSGA-II algorithm, the proposed algorithm yields better solutions and consumes less computing time.