Autonomous Interplanetary Trajectory Planning Using Structured-Chromosome Evolutionary Algorithms

Nyew, Hui Meen; Abdelkhalik, Ossama; Onder, Nilufer

doi:10.2514/6.2012-4522

Cited by 2 publications

(10 citation statements)

References 2 publications

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“…Furthermore, the adopted crossover operator aims at maximising the information exchange per crossover operation. However, in the preliminary stage of this research, the strategy proposed in [5] appeared to be too destructive, making the information contained in the parents disappear over the generations. For this reason, here the number of exchanging genes of each class is computed considering the structure of the two parents chromosomes.…”

Section: The Algorithmmentioning

confidence: 94%

“…In fact, swapping genes among parent chromosomes on the basis of their position may result in selecting genes that represent different variables and creating unfeasible, or even meaningless, solutions. For this reason a different strategy for selecting genes to swap based on their class has been proposed by [5].…”

Section: The Algorithmmentioning

confidence: 99%

“…A variety of strategies for facing variable-sized global optimisation can be found in the literature, mostly employed for space trajectory design [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…A more complex, but efficient adaptation of GA is proposed in [4], [5]. In these cases, a hierarchical multi-level chromosome structure is adopted in place of the standard string one.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation

Greco

Gentile

Filippi

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.

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Section: The Algorithmmentioning

confidence: 94%

Section: The Algorithmmentioning

confidence: 99%

“…A variety of strategies for facing variable-sized global optimisation can be found in the literature, mostly employed for space trajectory design [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

“…A more complex, but efficient adaptation of GA is proposed in [4], [5]. In these cases, a hierarchical multi-level chromosome structure is adopted in place of the standard string one.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation

Greco

Gentile

Filippi

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

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“…The literature already comprises a optimisation algorithms to cope with variablesize global optimisation mostly applied to space trajectory design (Nyew et al 2012(Nyew et al , 2015Darani and Abdelkhalik 2017). One relevant example is the hidden-genes GA variant introduced in Darani and Abdelkhalik (2017).…”

Section: Introductionmentioning

confidence: 99%

Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms

et al. 2021

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This paper focuses on the scheduling under uncertainty of satellite tracking from a heterogeneous network of ground stations taking into account allocated resources. An optimisation-based approach is employed to efficiently select the optimal tracking schedule that minimises the final estimation uncertainty. Specifically, the scheduling is formulated as a variable-size problem, and a Structured-Chromosome Genetic Algorithm is developed to tackle the mixed-discrete global optimisation. The search algorithm employs genetic operators specifically revised to handle hierarchical search spaces. An orbit determination routine is run within each call to the fitness function to quantify the estimation uncertainty resulting from each candidate tracking schedule. The developed scheduler is tested on the tracking optimisation of a satellite in low Earth orbit, a highly perturbed dynamical regime. The obtained results show that the variable-size variants of Genetic Algorithms always outperform the fixed-size counterparts employed for comparison. In particular, Structured-Chromosome Genetic Algorithm is shown to find significantly better schedules under severely limited budgets.

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Autonomous Interplanetary Trajectory Planning Using Structured-Chromosome Evolutionary Algorithms

Cited by 2 publications

References 2 publications

Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation

Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation

Stochastic satellite tracking with constrained budget via structured-chromosome genetic algorithms

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