Ant-based Mission Planning: Two Examples

Ntagiou, Evridiki V.; Iacopino, Claudio; Policella, Nicola; Armellín, Roberto; Donati, Alessandro

doi:10.2514/6.2018-2498

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Considering the drift-angle constraint, Du et al [8] provide an area target observation scheduling formulation and propose an ant colony algorithm to solve the problem. Ntagiou et al [9] design an automated mission scheduling model for an Earth imaging mission and a data relay mission. Ant colony optimization is then applied to address these problems.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…In practical satellite scheduling, the uncertainty, which origins from the change of mission priority, weather conditions and satellite resource status, is inevitable. Considerable researchers have studied the scheduling problem for both CEOS and AEOS, in which the uncertainty derived from dynamic missions and emergency responses have been considered [25,26,27,28,29]. On account of the widespread application of optical sensors on the EOS, the observation mission is extremely influenced by the uncertainty of cloud coverage [30].…”

Section: Introductionmentioning

confidence: 99%

“…The consumption of resource l for target i on orbit k is represented as Cost ilk , and the remaining amount of resource l on orbit k is denoted as Cap ik . In Equation(29), parameter p ik indicates the probability that target i will be successfully observed on orbit k. A larger p ik means that target i is more likely to observe successfully on orbit k. Then CF ik will be lower, which means that the corresponding resource will be allocated to the target earlier.The second part CF S ik |V T W ik | represents the proportion of the VTW of target i that overlaps with other VTWs on orbit k. The third part, m l=1 Cost ilk Cap lk , represents the proportion of the resource consumed by observing the target in the remaining resource. The larger values of these two parts, the later the observation of corresponding targets will be considered and scheduled.…”

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confidence: 99%

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

Chen

et al. 2020

Preprint

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Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

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“…Approximate algorithms are then adopted to approach a near optimal solution in a reasonable time frame. The intelligence algorithms including genetic algorithm [9,14,15], local search algorithm [16] and ant colony optimization [17] have been widely applied for the EOS scheduling. Besides, tremendous heuristic procedures have been introduced to arrange feasible EOS scheduling missions.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Multiple Agile Earth Observation Satellites for Oversubscribed Targets Using Complex Networks Theory

et al. 2019

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The Earth observation satellites (EOSs) scheduling is of great importance to achieve efficient observation missions. The agile EOSs (AEOS) with stronger attitude maneuvering capacity can greatly improve observation efficiency while increasing scheduling complexity. The multiple AEOSs, oversubscribed targets scheduling problem with multiple observations are addressed, and the potential observation missions are modeled as nodes in the complex networks. To solve the problem, an improved feedback structured heuristic is designed by defining the node and target importance factors. On the basis of a real world Chinese AEOS constellation, simulation experiments are conducted to validate the heuristic's efficiency in comparison with a constructive algorithm and a structured genetic algorithm.

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Ant-based Mission Planning: Two Examples

Cited by 6 publications

References 10 publications

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

Robust Earth Observation Satellite Scheduling With Uncertainty of Cloud Coverage

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

Scheduling Multiple Agile Earth Observation Satellites for Oversubscribed Targets Using Complex Networks Theory

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