Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

Simões, Luís; Izzo, Dario; Haasdijk, Evert; Eiben, A. E.

doi:10.1007/978-3-319-55453-2_10

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…Another stochastic tree search that hybridizes Beam Search with ACO is Beam P-ACO as introduced by Simões et al [52]. Following this approach, the search paths of the tree are modified by pheromone markers to reinforce exploitation of promising paths.…”

Section: Tree Searchesmentioning

confidence: 99%

A survey on artificial intelligence trends in spacecraft guidance dynamics and control

2019

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The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.

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Section: Tree Searchesmentioning

confidence: 99%

A survey on artificial intelligence trends in spacecraft guidance dynamics and control

2019

Self Cite

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“…The optimal control law can be obtained by the Pontryagin's maximum principle as (13) where * and * are the optimal control angles of solar sail, ̃ and ̃ are the angles that describe the direction of the velocity co-state as follows [34] [36]. ˆˆĉ os sin cos sin sin (14)…”

Section: B Optimal Control Problem Formulationmentioning

confidence: 99%

“…To reduce the cost of a single launch and increase the benefit of the spacecraft for NEA exploration, multiple targets exploration missions have significant roles. For such consideration, the Global Trajectory Optimization Competition (GTOC) has shown great interest in the mission design of multi-asteroid exploration, and over the 9 competitions organized to date, 4 were multi-asteroid rendezvous problems [14]. For example, the sponsor of GTOC 4 focused on the achievement of visiting as many NEAs as possible within a given time duration [15].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the sponsor of GTOC 4 focused on the achievement of visiting as many NEAs as possible within a given time duration [15]. When considering a multi-target exploration mission, the preliminary design of trajectory becomes challenging because of the complexity of combinatorial problem that emerges with the increasing number of targets to visit in the mission [14]. On the one hand, the cost of transfer between two adjacent candidate targets, such as fuel consumption or transfer time, usually requires to solve a complicated numerical optimization problem, which is particularly of low computational efficiency in the preliminary mission design [16].…”

Section: Introductionmentioning

confidence: 99%

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Solar-sail trajectory design for multiple near-Earth asteroid exploration based on deep neural networks

Song

Gong

2019

Aerospace Science and Technology

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In the preliminary trajectory design of the multi-target rendezvous problem, a model that can quickly estimate the cost of the orbital transfer is essential. The estimation of the transfer time using solar sails between two arbitrary orbits is difficult and usually requires to solve an optimal control problem. Inspired by the successful applications of the deep neural networks in nonlinear regression, this work explores the possibility and effectiveness of mapping the transfer time for solar sails from the orbital characteristics using the deep neural networks. Furthermore, the Monte Carlo Tree Search method is investigated and used to search the optimal sequence considering a multi-asteroid exploration problem. The obtained sequences from preliminary design will be solved and verified by sequentially solving the optimal control problem. Two examples of different application backgrounds validate the effectiveness of the proposed approach.

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“…These variables that determine the total number of variables in a solution are referred to as the architecture variables. [36,37,38,39,40,41], deterministic algorithms [42,43,44], or a combination of them [45,46]. Deterministic methods use grid or tree search to explore the design space.…”

Section: Space Trajectory Optimizationmentioning

confidence: 99%

System Architecture Optimization Using Hidden Genes Genetic Algorithms with Applications in Space Trajectory Optimization

Darani¹

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In this dissertation, the concept of hidden genes genetic algorithms is developed. In system architecture optimization problems, the topology of the solution is unknown and hence, the number of design variables is variable. Hidden genes genetic algorithms are genetic algorithm based methods that are developed to handle such problems by hiding some genes in the chromosomes. The genes in the hidden genes genetic algorithms evolve through selection, mutation, and crossover operations. To determine if a gene is hidden or not, binary tags are assigned to them. The value of the tags determine the status of the genes. Different mechanisms are proposed for the evolution of the tags. Some mechanisms utilize stochastic operations while others are based on deterministic operations. All the proposed mechanisms are tested on mathematical and space trajectory optimization problems. Moreover, Markov chain models of the mechanisms are derived and their convergence is investigated analytically. The results show that the proposed concept are capable to search for the optimal solution by autonomously enabling the algorithms to assign the hidden genes.

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Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

Cited by 23 publications

References 22 publications

A survey on artificial intelligence trends in spacecraft guidance dynamics and control

A survey on artificial intelligence trends in spacecraft guidance dynamics and control

Solar-sail trajectory design for multiple near-Earth asteroid exploration based on deep neural networks

System Architecture Optimization Using Hidden Genes Genetic Algorithms with Applications in Space Trajectory Optimization

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