DRL-based Optimal Scheduling for On-orbit Service with the Encoder-decoder network

In this paper, we present a deep reinforcement learning (DRL) based strategy for optimizing the scheduling of satellite on-orbit services. The orbital maneuvers necessitate the servicing satellite to consecutively rendezvous with multiple targets to execute its on-orbit missions. The principal aim o...

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Veröffentlicht in:Chinese Control Conference S. 8786 - 8791
Hauptverfasser: Zhang, Jierui, Si, Chaoming, Ma, Changbo, Chen, Ting, Xia, Hongwei, Ma, Guangcheng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Schlagworte:
Costs
Deep reinforcement learning
Encoder-decoder neural network
Metaheuristics
Neural networks
Optimal scheduling
Processor scheduling
Satellite on-orbit service
Satellites
Traveling salesman problems
ISSN:1934-1768
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Zusammenfassung:In this paper, we present a deep reinforcement learning (DRL) based strategy for optimizing the scheduling of satellite on-orbit services. The orbital maneuvers necessitate the servicing satellite to consecutively rendezvous with multiple targets to execute its on-orbit missions. The principal aim of our optimization approach is to ascertain the most advantageous sequence for servicing satellites, thereby minimizing the overall cost, contingent upon the expenditure of propulsion maneuvers. To surmount this formidable challenge, we introduce an attention-based encoder-decoder neural network and train its parameters utilizing the REINFORCE algorithm with a greedy rollout baseline. Ultimately, experimental results across diverse scenarios validate the efficacy and supremacy of our proposed algorithm. The chief contribution of this work lies in its conceptualization of the satellite on-orbit service scheduling optimization quandary as an extended traveling salesman problem, culminating in the introduction of an innovative DRL-based methodology.
ISSN:1934-1768
DOI:10.23919/CCC63176.2024.10662023