Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn

Zotes, Fernando Alonso; Santos, Matilde

doi:10.1016/j.engappai.2011.09.005

Cited by 27 publications

(3 citation statements)

References 25 publications

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“…In [4], the authors extended the standard PSO algorithm to a multi-objective version, thereby constructing a multi-objective PSO (MOPSO) algorithms. This extended algorithm was then applied to address a Earth-Jupiter-Saturn orbital transfer problem and the results illustrated the feasibility as well as the reliability of the proposed method.…”

Section: Multi-objective Transcription-based Techniquesmentioning

confidence: 99%

“…Among them, the development of spacecraft technology has attracted significant attention [1,2]. So far, several generations of spacecraft have been designed, manufactured, launched, and successfully implemented in different mission profiles such as communications [3], interplanetary travel [4], regional reconnaissance [5], environmental monitoring [6], and so on. Because of the long development cycle, high operating cost, and limited resources, it is usually desired by aerospace engineers that the space vehicle can fulfill the mission with some performance metrics to be optimized, or in other words, in an optimal or near-optimal way.…”

Section: Introductionmentioning

confidence: 99%

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A review of optimization techniques in spacecraft flight trajectory design

Chai

Tsourdos

Chai

et al. 2019

Progress in Aerospace Sciences

110

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For most atmospheric or exo-atmospheric spacecraft flight scenarios, a welldesigned trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective

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Section: Multi-objective Transcription-based Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of optimization techniques in spacecraft flight trajectory design

Chai

Tsourdos

Chai

et al. 2019

Progress in Aerospace Sciences

110

View full text Add to dashboard Cite

show abstract

“…The concept of a hidden-genes GA for multi-objective optimization is used in [36], constraining the bounds of the launch date to a one-month range and assuming a maximum of three fly-bys for missions to Mercury and Jupiter, and a maximum of four fly-bys for missions to Saturn. A multi-objective variant of both ACO [37] and PSO [38] has been employed to optimize MGA transfers with the knowledge of the planetary sequence. An agent-based mimetic algorithm has also been introduced [39] for multi-objective optimization assuming a priori the MGA sequence used to reach Saturn.…”

mentioning

confidence: 99%

Multiobjective Design of Gravity-Assist Trajectories via Graph Transcription and Dynamic Programming

Bellome

Sánchez

Felicetti

et al. 2023

Journal of Spacecraft and Rockets

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Multiple gravity-assist (MGA) trajectory design requires the solution of a mixed-integer programming problem to find the best sequence among all possible combinations of candidate planets and dates for spacecraft maneuvers. Current approaches require computing times rising steeply with the number of control parameters, and they strongly rely on narrow search spaces. Moreover, the challenging multiobjective optimization needs to be tackled to appropriately inform the mission design with full extent of launch opportunities. This paper describes a methodology based upon a trajectory model to transcribe the mixed-integer space into a discrete graph made by grids of interconnected nodes. The model is based on Lambert arc grids obtained for a range of departure dates and flight times between two planets. A Tisserand-based criterion selects planets to pass by. Dynamic programming is extended to multiobjective optimization of MGA trajectories and used to explore the graph, guaranteeing Pareto optimality with only moderate computational effort. Robustness is ensured by evaluating the relationship between graph and mixed-integer spaces. Missions to Jupiter and Saturn alongside challenging comet sample return transfers involving long MGA sequences are discussed. These examples illustrate the robustness and efficiency of the proposed approach in capturing globally optimal solutions and wide Pareto fronts on complex search spaces.

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