Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

Chen, Han; Gu, Yi; Wu, Guohua; Wang, Xinwei

doi:10.48550/arxiv.2003.08363

Cited by 3 publications

(7 citation statements)

References 56 publications

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“…A branch-and-cut (B&C) algorithm was proposed subsequently to solve the converted MIP model. Han et al [16] popularized the CCP model into the more difficult AEOS mission planning problem under cloud coverage uncertainty. Relying on the concept of time slack, an optimization subproblem was established to determine the start time for each observation.…”

Section: A Eos Mission Planning Under Cloud Coverage Uncertaintymentioning

confidence: 99%

“…In line with [16] and [23], we adopt the CCP model to implement proactive uncertain mission planning. Some notations in this study are summarized in Table II.…”

Section: Notationsmentioning

confidence: 99%

“…Therefore, the main goal of this study is to investigate the mission planning problem of multiple EOSs based on cloud coverage forecasting. In line with [16], proactive mission planning for multiple agile EOSs (AEOSs) based on the CCP model has been initially implemented. Subsequently, the true cloud information is introduced to the mission planning framework, which could help train the cloud forecasting model and evaluate the result of mission planning.…”

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confidence: 99%

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Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recent decades have witnessed a tremendous growth in the number of Earth observation satellites (EOSs), which presents a huge challenge for mission planning. For the EOSs with optical sensors particularly, the observation mission is significantly influenced by the uncertainty of cloud coverage, which has been identified as the most dominant factor for the invalidation of remote sensing images. To overcome this uncertainty, uncertainty programming methods, namely, chance constraint programming (CCP), stochastic expectation model, and robust optimization, are put forth. Despite their success, these approaches are limited in that they simplified the complex cloud coverage uncertainty, which may be different from the true cloud conditions, and they did not take the true cloud information into consideration. Motivated by these recent trends toward Big Data of satellite cloud images and machine learning for spatiotemporal prediction, this article explores a dynamic replanning scheme for multiple EOSs based on cloud forecasting. Specifically, we propose a new approach mainly in the following three steps: first, proactive scheduling based on a CCP is implemented and uploaded via ground control; second, cloud forecasting can be continuously conducted relying on the predictive recurrent neural network and the latest satellite cloud image; and third, mission replanning can be conducted according to the initial schedule and relatively accurate cloud information. Simulation results show that the cloud forecasting method is effective, and the replanning approach presents highly efficient and accurate scheduling results.

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Section: A Eos Mission Planning Under Cloud Coverage Uncertaintymentioning

confidence: 99%

“…In line with [16] and [23], we adopt the CCP model to implement proactive uncertain mission planning. Some notations in this study are summarized in Table II.…”

Section: Notationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…We denote the attitude transition time between adjacent observation strips as st l 1 l 2 j for simplicity afterward. Taking the attitude stabilization process into account [41], [42], the attitude transition time can be formulated as…”

Section: B Notationsmentioning

confidence: 99%

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Gu¹,

Chen²,

Chen³

et al. 2022

Preprint

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The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this paper aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the evolution process. In the end, extensive experiments are conducted to illustrate the effectiveness and reliability of the proposed method. Compared to the traditional particle swarm optimization and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.

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“…Although the greedy heuristic suffers from premature convergence in finding optimal solution, it is still a simple but efficient method to construct elite initial schedule [13]. To avoid being trapped in the local optima, Han [14] and Sarkheyli [15] utilize simulated annealing and tabu search to improve the possibilities to accept worse solution. Based on heuristic rules, Chen et al [16] destroy the current solution to restart the search process.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Xiong

Zheng

2022

IEEE Access

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Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-ofthe-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

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Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty

Cited by 3 publications

References 56 publications

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

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