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 sequen...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 58; H. 3; S. 2304 - 2326
Hauptverfasser: Wei, Zhao, Long, Teng, Shi, Renhe, Wu, Yufei, Ye, Nianhui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Aerodynamics
Chemical propulsion
Cluster analysis
Clustering
Continuity (mathematics)
Evolutionary algorithms
Evolutionary computation
Geostationary missions
Missions
multiple hybrid-propulsive spacecraft
Mutation
Optimization
Orbital maneuvers
Orbits
Propellant consumption
Propulsion
Propulsion systems
Scheduling
scheduling optimization
Space debris
Space debris mitigation
space debris removal
Space vehicles
Spacecraft
Task scheduling
Vector quantization
Vehicle dynamics
ISSN:0018-9251, 1557-9603
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2021.3131294