Increasing efficiency of ground stations scheduling for sustainable satellite based services

Kucherov, Boris; Přibyl, Ondřej

doi:10.1109/scsp.2018.8402657

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…For the sake of solving the scheduling problem, Wei and Xiu-li [25] established a bidding mechanism with an honesty and termination-contract strategy, while Xhafa, Barolli, and Takizawa [26] proposed the Steady State Genetic Algorithm (SSGA). In order to analyse possibilities to increase the quality of schedule and efficiency of its adaptations, Kucherov and Přibyl [27] discussed mathematical formulation of TT&C ground stations scheduling problem. Moreover, Lala et al [28] compared the effectiveness of local search with Population-based search for the ground stations scheduling problem.…”

Section: Research On Ground Stations Scheduling Problemmentioning

confidence: 99%

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Chen

Xiao

Jicheng

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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With the rapidly increasing number and movement of satellites, the ground stations scheduling problem for Low Earth Orbit (LEO) satellites becomes more complex due to its over-constraint nature. In this paper, we propose a novel ground stations scheduling algorithm named Reinforcement Learning-Based Ground Stations Scheduling Algorithm (RLBGSSA). Its batch tasks planning branch (RLBGSBSA) ensures ground stations efficiently track satellites and achieve timeliness. Under the state constraints, the individual task planning branch of RLBGSSA (RLBGSISA) generates the feasible planning combinations based on reinforcement learning, and gains the combination constraints to filter the planning combinations. To maintain the stability of tasks and equipment, the optimal combination among the screened results is selected via a designed value function, which can achieve minimal changes to planned tasks. Furthermore, RLBGSBSA can reset the scheduling of ground stations to void planning overflow, when the number of individual tasks newly added is too large. Finally, the rationality and effectiveness of our algorithm are verified by numerical simulation.

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Section: Research On Ground Stations Scheduling Problemmentioning

confidence: 99%

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Chen

Xiao

Jicheng

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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Intelligent Management of Satellite Ground System

Tian

Huang

et al. 2021

2021 33rd Chinese Control and Decision Conference (CCDC)

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Formulation of the problem of ground stations scheduling under resource restrictions

Kucherov

2018

Inf.-tehnol. vestn.

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The tasks solving during ground stations scheduling to ensure satellites control are considered. The hierarchy of scheduling problems is analyzed. Ground stations scheduling problem is compared with general scheduling problem. Various approaches to develop schedule of ground stations are analyzed. Mathematical formulation of ground stations scheduling problem is presented. Research of possible ways to increase efficiency and quality of ground stations scheduling was performed using presented mathematical formulation of mentioned problem.

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Increasing efficiency of ground stations scheduling for sustainable satellite based services

Cited by 4 publications

References 12 publications

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Intelligent Management of Satellite Ground System

Formulation of the problem of ground stations scheduling under resource restrictions

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