Onboard autonomous mission re-planning for multi-satellite system

Zheng, Zixuan; Guo, Jian; Gill, Eberhard

doi:10.1016/j.actaastro.2018.01.017

Cited by 34 publications

(15 citation statements)

References 18 publications

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“…Another valuable avenue for future research is a more accurate modelling of the data transmission constraints (uplink and downlink). Finally, autonomous satellite scheduling systems have received significant attention recently [55,56]; combining planning methods under uncertainty with autonomous planning platforms is also a promising opportunity for future engineering practice.…”

Section: Discussionmentioning

confidence: 99%

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

Wang

Song

Leus

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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Earth observation satellites (EOSs) are specially equipped with remote sensing instruments to acquire images. In practical EOS scheduling, the uncertainty of cloud coverage is inevitable. We are the first to address robust EOS scheduling under uncertainty due to cloud coverage where the objective function aims to maximize the entire observation profit. We provide a robust formulation of the scheduling problem on the basis of a budgeted uncertainty set, while preserving the formulation linearity. A columngeneration-based heuristic is also developed, in which the scheduling decisions in each satellite orbit are represented as columns. Eventually, a high-quality feasible solution is obtained using the generated columns. Extensive simulations are conducted on the basis of one of China's EOS constellations. The results indicate that the average optimality gap is less than 5%, which validates the performance of the proposed heuristic.

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Section: Discussionmentioning

confidence: 99%

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

Wang

Song

Leus

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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“…Jiang and Pang [21] designed an adaptive ant colony algorithm by designing a task merging mechanism to solve the problem of huge user requests. Zheng et al [22] combined the re-planning technique with the multi-objective hybrid dynamic mutation genetic algorithm for dynamic re-planning scenarios of different urgency levels and designed the cyclic re-planning algorithm and the near-real-time re-planning algorithm. Niu [23] used genetic algorithms to solve dynamic task planning problems.…”

Section: Overview Of Satellite Task Planning Algorithmsmentioning

confidence: 99%

A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

Liu

Yang

2019

Symmetry

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The intelligent satellite, iSAT, is a concept based on software-defined satellites. Earth observation is one of the important applications of intelligent satellites. With the increasing demand for rapid satellite response and observation tasks, intelligent satellite in-orbit task planning has become an inevitable trend. In this paper, a mixed integer programming model for observation tasks is established, and a heuristic search algorithm based on a symmetric recurrent neural network is proposed. The configurable probability of the observation task is obtained by constructing a structural symmetric recurrent neural network, and finally, the optimal task planning scheme is obtained. The experimental results are compared with several typical heuristic search algorithms, which have certain advantages, and the validity of the paper is verified. Finally, future application prospects of the method are discussed.

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“…The main drawback of max-sum is that it cannot adapt to large-scale UAV swarm and online re-planning in dynamic environments. Genetic algorithm (GA) [12,13]is another commonly used algorithm. According to biological evolution theory, mechanisms such as selection, evolution, and mutation can be used to improve the fitness of individuals.…”

Section: Introductionmentioning

confidence: 99%

UAV Cooperative Search in Dynamic Environment Based on Hybrid-Layered APF

Shao

Tao

Liu

et al. 2021

Preprint

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Unmanned Aerial Vehicle (UAV) detection has the advantages of flexible deployment, no casualties. It has become a force that cannot be ignored in the battlefield. Scientific and efficient mission planning can help improving the survival rate and mission completion rate of the UAV search in dynamic environments. Towards the mission planning problem of UAV collaborative search for multi-types of time-sensitive moving targets, a search algorithm based on hybrid layered artificial potential fields algorithm (HL-APF) was proposed. This paper utilized a new target attraction field function which was segmented by the search distance to quickly search for quickly targets searching. Moreover, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Besides, in order to solve the unknown target search and improve the area coverage, a centralized layered scheduling algorithm controlled by the cloud server (CS) was added. CS divides the mission area into several sub-areas, and allocates UAV according to the priority function based on the search map. The CS activation mechanism can make full use of prior information, and the UAV assignment cool-down mechanism can avoid the repeated assignment of the same UAV. The simulation results show that compared with the hybrid artificial potential field and ant colony optimization (HAPF-ACO) and improved artificial potential field(IAPF), this method can significantly improve the found number of targets and mission area coverage. At the same time, comparative experiment results of CS mechanism proved the necessity of setting CS activation and cool-down for improving the search performance.

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Onboard autonomous mission re-planning for multi-satellite system

Cited by 34 publications

References 18 publications

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network

UAV Cooperative Search in Dynamic Environment Based on Hybrid-Layered APF

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