Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories

Izzo, Dario; Becerra, Victor M.; Myatt, D. R.; Nasuto, Slawomir J.; Bishop, J. M.

doi:10.1007/s10898-006-9106-0

Cited by 93 publications

(58 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The problem has, in this case, a finite dimension equal to two and can be solved very efficiently both by the use of heuristic global optimization techniques and by using deterministic global optimizers [23]. Here we apply differential evolution [24] to search the solution space defined as I 2015; 2029 2015; 2029.…”

Section: Example 2: Case Of 99942 Apophismentioning

confidence: 99%

Optimization of Interplanetary Trajectories for Impulsive and Continuous Asteroid Deflection

Izzo

2007

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

The chaotic dynamic of near earth objects and our limited knowledge on the asteroid population makes it impossible to predict the next impact of an asteroid with our planet. The collision at Jupiter of the comet ShoemakerLevy 9, the more recent discovery of the asteroid 99442 Apophis, and other similar events, have stimulated the discussion on our capabilities of deflecting an asteroid in collision route with Earth. We propose a simple expression to evaluate the merit of a given deflection strategy. We then specialize the derived analytical expression in the special cases of a kinetic impactor and of a long duration thrust deflection. We then study two examples. The first deals with a massive spacecraft equipped with an advanced low-thrust propulsion system. We show, fictitiously, that it is convenient to send such a spacecraft to impact the asteroid rather than to rendezvous and push it. In the second example, we solve the optimal interplanetary trajectory problem to deflect the asteroid 99942 Apophis. We find that several optimal interplanetary trajectories exist that allow a 750-Kg spacecraft to be launched from 2015-2026, and impact 99942 Apophis allowing prekeyhole deflections of orders of magnitude larger than the likely required amount. Nomenclature At= deflection strategy a = asteroid semimajor axis f = focusing factor gt = gravitational and disturbance forces acting on the asteroid i v = velocity unit vector i p = unit vector inertially fixed in the heliocentric frame M = mean anomaly m = mass m c = fuel mass available at asteroid rendezvous N = number of available observations n = mean motion p = asteroid momentum Q = function of the squared residuals used to define the virtual asteroid R E = Earth orbit radius t = encounter time t c = coast time t p = deflection duration or push time t s = time to encounter when the deflection starts

show abstract

Section: Example 2: Case Of 99942 Apophismentioning

confidence: 99%

Optimization of Interplanetary Trajectories for Impulsive and Continuous Asteroid Deflection

Izzo

2007

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

show abstract

“…This problem consists in finding suitable parameter values which are used to define a trajectory for a space vehicle. The literature on this subject is quite vast -we refer the readers, e.g., to (Addis, Cassioli, Locatelli, & Schoen, 2009;Izzo, Becerra, Myatt, Nasuto, & Bishop, 2007;Olympio & Marmorat, 2008;Vasile, 2005) for references on this subject. Here it may suffice to say that this is a family of very hard global optimization problems with relatively few variables (a few tens at most) but a huge amount of local optima.…”

Section: Space Trajectory Designmentioning

confidence: 99%

Machine learning for global optimization

et al. 2010

View full text Add to dashboard Cite

In this paper we introduce the LeGO (Learning for Global Optimization) approach for global optimization in which machine learning is used to predict the outcome of a computationally expensive global optimization run, based upon a suitable training performed by standard runs of the same global optimization method. We propose to use a Support Vector Machine (although different machine learning tools might be employed) to learn the relationship between the starting point of an algorithm and the final outcome (which is usually related to the function value at the point returned by the procedure). Numerical experiments performed both on classical test functions and on difficult space trajectory planning problems show that the proposed approach can be very effective in identifying good starting points for global optimization.

show abstract

“…Solórzano et al [3] discuss the planning 2 International Journal of Aerospace Engineering of trajectories to Neptune using a variety of gravity assist maneuver schemes, highlighting the difficulty in finding an optimized solution with so many combinations of the planets considered. In another valuable study, Izzo et al [4] discuss a deterministic search space pruning algorithm for multiple gravity assist problem. Vasile and Pascale [5] investigates a preliminary design of multiple gravity assist maneuvers as a global optimization method based on a hybrid genetic algorithm methodology.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Determination and Mission Design for Asteroid/Deep-Space Exploration via Multibody Gravity Assist Maneuvers

Fritz

Turkoglu

2017

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

This paper discusses the creation of a genetic algorithm to locate and optimize interplanetary trajectories using gravity assist maneuvers to improve fuel efficiency of the mission. The algorithm is implemented on two cases: (i) a Centaur-class target close to the ecliptic plane and (ii) a Centaur-class target with a high inclination to the ecliptic plane. Cases for multiple numbers of flybys (up to three) are discussed and compared. It is shown that, for the targets considered here, a single flyby of Jupiter is the most efficient trajectory to either target with the conditions and limitations discussed in this paper. In this paper, we also iterate on possible reasons for certain results seen in the analysis and show how these previously observed behaviors could be present in any trajectory found. The parameters and methods used in the algorithm are explained and justified over multiple real-life interplanetary missions to provide deeper insights into the development choices.

show abstract

Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories

Cited by 93 publications

References 9 publications

Optimization of Interplanetary Trajectories for Impulsive and Continuous Asteroid Deflection

Optimization of Interplanetary Trajectories for Impulsive and Continuous Asteroid Deflection

Machine learning for global optimization

Optimal Trajectory Determination and Mission Design for Asteroid/Deep-Space Exploration via Multibody Gravity Assist Maneuvers

Contact Info

Product

Resources

About