Ground station activity planning through a multi-algorithm optimisation approach

Corrao, Giuseppe; Falone, Roberta; Gambi, Ennio; Spinsante, Susanna

doi:10.1109/estel.2012.6400125

Cited by 12 publications

(10 citation statements)

References 6 publications

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“…Wang et al [23] analyzed intelligent algorithms' adaptability and optimization results in different situations. To provide the most efficient configurations, Corrao et al [24] presented a planning approach to support the automatic allocation of contact opportunities among ground stations and a set of satellites. 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).…”

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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“…Gwangju Institute of Science and Technology (GIST) and KARI presented a solution to the conflict problem of visibility using multi-antenna scheduling (MAS) [27] and a genetic algorithm [28]. Corrao et al [29] proposed a resolution of the visibility conflict between multiple satellites and a single ground antenna by using a wide spectrum of algorithms. However, their research did not consider antenna support time and real operation modes.…”

Section: Previous Workmentioning

confidence: 99%

Genetic algorithm-based scheduling for ground support of multiple satellites and antennae considering operation modes

Lee¹,

Kim²,

Chung³

et al. 2016

International Journal of Aeronautical and Space Sciences

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Given the unpredictability of the space environment, satellite communications are manually performed by exchanging telecommands and telemetry. Ground support for orbiting satellites is given only during limited periods of ground antenna visibility, which can result in conflicts when multiple satellites are present. This problem can be regarded as a scheduling problem of allocating antenna support (task) to limited visibility (resource). To mitigate unforeseen errors and costs associated with manual scheduling and mission planning, we propose a novel method based on a genetic algorithm to solve the ground support problem of multiple satellites and antennae with visibility conflicts. Numerous scheduling parameters, including user priority, emergency, profit, contact interval, support time, remaining resource, are considered to provide maximum benefit to users and real applications. The modeling and formulae are developed in accordance with the characteristics of satellite communication. To validate the proposed algorithm, 20 satellites and 3 ground antennae in the Korean peninsula are assumed and modeled using the satellite tool kit (STK). The proposed algorithm is applied to two operation modes: (i) telemetry, tracking, and command and (ii) payload. The results of the present study show near-optimal scheduling in both operation modes and demonstrate the applicability of the proposed algorithm to actual mission control systems.

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“…In research on scheduling communications for the MS-MG, Vazquez et al [5] have suggested the integer linear programming (ILP) model for an antenna allocation problem considering preferred assignments and priority, and Wang et al [6] have proposed an ILP model considering the uncertainty of cloud coverage. Corrao et al [7] have presented an optimization strategy for assigning communication opportunities by integrating GA, graph theory, and linear programming. Xhafa et al [8] have suggested a GA for ground-station allocation problems to maximize the number of communications, communication time, and communication ground stations and minimize the number of communication collisions between satellites.…”

Section: Introductionmentioning

confidence: 99%

Mixed-Integer Linear Programming Model for Scheduling Missions and Communications of Multiple Satellites

Lee,

Yu,

Kwon

et al. 2024

Aerospace

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Satellites have been developed and operated for various purposes. The global satellite market is growing rapidly as the number of satellites and their mission diversity increase. Satellites revolve around the Earth to perform missions and communicate with ground stations repeatedly and sequentially. However, because satellites are orbiting the Earth, there is a limited time window for missions to a specific area and communication with ground stations. Thus, in an environment where multiple satellites and multiple ground stations (MS-MGs) are operated, scheduling missions and communications to maximize the utilization of satellites is a complex problem. For the MS-MG scheduling problem, this study proposes a mixed-integer linear programming (MILP) model to assign time windows for missions and communications with ground stations to individual satellites. The MILP model is based on the concept of a time-space network and includes constraints reflecting on the space mission environment of satellites. The objective function and constraints of the MILP model were validated through numerical experiments based on actual data from Korean satellites.

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Ground station activity planning through a multi-algorithm optimisation approach

Cited by 12 publications

References 6 publications

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Reinforcement learning-based ground stations scheduling algorithm for LEO satellites

Genetic algorithm-based scheduling for ground support of multiple satellites and antennae considering operation modes

Mixed-Integer Linear Programming Model for Scheduling Missions and Communications of Multiple Satellites

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