Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

Savitri, Tania; Kim, Youngjoo; Jo, Sujang; Bang, Hyochoong

doi:10.1155/2017/1235692

Cited by 52 publications

(27 citation statements)

References 22 publications

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“…as the parameter optimised is coverage, although global [2]. Both studies only consider The final satellite constellation obtained using NSGA-II has 100% coverage over the to the detection algorithm results (See Figures 10, 11 and 12).…”

Section: Discussionmentioning

confidence: 99%

An Optimised Satellite Constellation for Bushfire Detection through Edge Computing

Wijayatunga¹,

Wu²

2021

Preprint

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The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.

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Section: Discussionmentioning

confidence: 99%

An Optimised Satellite Constellation for Bushfire Detection through Edge Computing

Wijayatunga¹,

Wu²

2021

Preprint

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“…In the current state of the art, constellations are designed using the grid point method [46] where the target region is specified by a grid of points on the target body surface. The performance of constellations of different shapes and structures are then tested either by varying them manually [48] or through a computer-based optimization scheme [49,50]. However, constellation swarms have not been well studied for mapping of small bodies.…”

Section: Related Workmentioning

confidence: 99%

Attitude Control of Spacecraft Swarms for Visual Mapping of Planetary Bodies

Nallapu

Thangavelautham

2019

2019 IEEE Aerospace Conference

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Planetary bodies such as asteroids, comets, and planetary moons are high-value science targets as they hold important information about the formation and evolution of our solar system. However, due to their low-gravity, variable sizes and shapes, dedicated orbiting spacecraft missions around these target bodies is difficult. Therefore, many planetary bodies are observed during flyby encounters, and consequently, the mapping coverage of the target body is limited. In this work, we propose the use of a spacecraft swarm to provide complete surface maps of a planetary body during a close encounter flyby. With the advancement of low-cost spacecraft technology, such a swarm can be realized by using multiple miniature spacecraft. The design of a swarm mission is a complex multi-disciplinary problem. To get started, we propose the Integrated Design Engineering & Automation of Swarms (IDEAS) software. In this work, we will introduce the development of the Automated Swarm Designer module of the software. The Automated Swarm Designer module will use evolutionary algorithms to optimize the design of swarms. The designed swarm will use 2 attitude control strategies to map the surface of a target body, namely: Nadir Pointing (NP), and Field of View Sweeping (FoV Sweeping). In the former strategy, the spacecraft are commanded to passively observe the sub-satellite point on the target body whenever the target is in the field of view of the individual spacecraft. This strategy is used when the observing instrument on board the spacecraft is large enough to capture the target from the desired encounter distance. In case the instrument is not large enough, then the spacecraft will have to maneuver their field of view to improve the coverage. The Field of View Sweeping strategy describes one such maneuver to improve coverage. In this strategy, the spacecraft in the swarm are commanded to sweep their fields of view about their principal axis normal to the swarm plane.The current work addresses different aspects of the swarm design problem. First, the coverage problem is addressed, and the parameters: instrument size, and target flyby distance are determined. Following this, the modeling of the swarm is described, where the individual spacecraft are arranged to define a circular plane around the target body during the encounter. Then the encounter trajectories of the spacecraft swarm are modeled. Here a Newton-Raphson iterative scheme using the state transition matrix of the dynamics is proposed for the entire swarm, which finds the trajectories between the desired starting points and destination points of the spacecraft within a specified flyby duration. Following this, the two proposed attitude control strategies are then described as they occur on these flyby trajectories. Then the design of these swarms is presented using as 2 optimization problems. The proposed strategies are used to develop a numerical simulator which will serve as the Automated Swarm Designer module of IDEAS for visual mapping missions. Finally, the simulator d...

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“…Generally, the orbit maneuver optimization problem can be treated as a kind of orbit design problem [4][5][6]. Numerous studies have been carried out to investigate the orbit design problem.…”

Section: Introductionmentioning

confidence: 99%

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Luo

Peng

et al. 2022

Remote Sensing

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Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

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Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

Cited by 52 publications

References 22 publications

An Optimised Satellite Constellation for Bushfire Detection through Edge Computing

An Optimised Satellite Constellation for Bushfire Detection through Edge Computing

Attitude Control of Spacecraft Swarms for Visual Mapping of Planetary Bodies

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

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