Scheduling Optimization of Multiple Hybrid-Propulsive Spacecraft for Geostationary Space Debris Removal Missions

To efficiently implement space debris removal missions on geostationary Earth orbit (GEO), a scheduling optimization scheme of multiple hybrid-propulsive servicing spacecraft (SSc) is proposed. In this scheme, a many-to-many task allocation coding model is established to formulate the removal sequences of debris targets with respect to different SSc. And a hybrid-propulsive orbit transfer strategy is developed to implement the debris removal maneuvers to save the propellant consumption and removal time simultaneously via using both electric and chemical propulsion systems. Then, the multiple GEO space debris removal scheduling optimization problem is formulated to minimize the total propellant consumption by exploring the optimal removal sequences and orbit maneuver parameters, subject to the available velocity increment, removal time, and many-to-many task allocation constraints. To effectively solve the scheduling optimization problem involving discrete-continuous mixed variables, a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed. In CADE-PIR, the k-means clustering method and potential individual reservation mutation strategy are employed to promote the convergence rate and performance of exploration and exploitation. In the end, a real-world GEO space debris removal mission with multiple SSc is investigated to demonstrate the effectiveness and practicality of the proposed optimization scheme.

Automated summary

This article proposes a scheduling optimization scheme for efficiently implementing space debris removal missions in geostationary Earth orbit (GEO). The scheme formulates the removal sequences of debris targets and develops a hybrid-propulsive orbit transfer strategy to save propellant consumption and removal time. The scheduling optimization problem is then formulated to minimize total propellant consumption, and a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed to solve it. The effectiveness and practicality of the proposed optimization scheme is demonstrated through a real-world GEO space debris removal mission.