Automated Interplanetary Mission Planning

Englander, Jacob A.; Conway, Bruce A.

doi:10.2514/5.9781624102714.0669.0706

Cited by 7 publications

(15 citation statements)

References 33 publications

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“…Bio-inspired techniques for the solution of the MGAP can be found in [3], [6], [5]. In [3] the authors proposed a hybrid branch & prune and evolutionary process that could automatically generate sequence and optimal multi-gravity assist transfer with Deep Space Maneuvers (DSM's) in a single loop.…”

Section: Introductionmentioning

confidence: 99%

“…In [3] the authors proposed a hybrid branch & prune and evolutionary process that could automatically generate sequence and optimal multi-gravity assist transfer with Deep Space Maneuvers (DSM's) in a single loop. In [6] and [5] the problem is approached by using Hidden Genes Genetic Algorithms (HGGA) where each sequence is represented by a binary chromosome. The generation of an optimal solution goes through two loops:…”

Section: Introductionmentioning

confidence: 99%

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A multidirectional Physarum solver for the automated design of space trajectories

Masi

Vasile

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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This version is available at https://strathprints.strath.ac.uk/53956/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract-This paper proposes a bio-inspired algorithm to automatically generate optimal multi-gravity assist trajectories. The multi-gravity assist problem has some analogies with the better known Traveling Salesman Problem and can be addressed with similar strategies. An algorithm drawing inspiration from the Physarum slime mould is proposed to grow and explore a tree of decisions that corresponds to the possible sequences of transfers from one planet to another. Some examples show that the proposed bio-inspired algorithm can produce solutions that are better than the ones generated by humans or with Hidden Genes Genetic Algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multidirectional Physarum solver for the automated design of space trajectories

Masi

Vasile

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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“…Recent research in low-thrust trajectory optimization has led to the creation of stochastic search methods that do not require an initial guess [32,33,34,28,17,11,35,18,36,37,38,19]. These methods are naturally suited for use in an HOCP framework.…”

Section: A Stochastic Global Search Via Monotonic Basin Hopping and mentioning

confidence: 99%

“…A problem may have several such funnels. MBH was originally developed to solve molecular conformation problems in computational chemistry, but has been demonstrated to be effective on various types of interplanetary trajectory problems [16,17,40,35,18,36,41]. MBH is a two-stage solver that alternates back and forth between a stochastic global search stage and a deterministic NLP stage.…”

Section: A Stochastic Global Search Via Monotonic Basin Hopping and mentioning

confidence: 99%

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Englander

Conway

2017

Journal of Guidance, Control, and Dynamics

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Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated, which can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a hybrid optimal control problem. The method is demonstrated on hypothetical missions to Mercury, the main asteroid belt, and Pluto.

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“…The DSMPGA was demonstrated to have an advantage, as compared to parallel processing of fixed-size populations, in terms of computational cost [9]. Englander et al [10,11] presented an approach that uses nested loops composed of an outer-loop solver (that handles the solution sequence in terms of the categorical variables) and an inner-loop solver (that finds the optimal trajectory for a given sequence). A binary genetic algorithm is used in the outer loop, and a cooperative algorithm based on particle swarm optimization and differential evolution is used in the inner loop.…”

mentioning

confidence: 99%

Structured-Chromosome Evolutionary Algorithms for Variable-Size Autonomous Interplanetary Trajectory Planning Optimization

Nyew

Abdelkhalik

Onder

2015

Journal of Aerospace Information Systems

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In interplanetary trajectory optimization, events such as planetary gravitational-assist maneuvers (swingbys) and deep-space maneuvers can be added/removed from the trajectory plan to reduce the cost or the flight time. This renders the number of design variables in the optimization problem variable. Global optimization methods that optimize this type of multimodal objective function can only handle problems with a fixed number of design variables. This paper presents the structured-chromosome evolutionary algorithm framework that is developed to handle variable-size design space optimization problems. In this framework, a solution (chromosome) is represented by a hierarchical data structure where the genes in the chromosome are classified as dependent and nondependent genes. This structure provides the capability to apply genetic operations between solutions of different lengths, and thus to automatically determine the number of swingbys, the planets to swingby, launch and arrival dates, and the number of deep-space maneuvers, as well as their locations, magnitudes, and directions, in an optimal sense. This new method is applied to several interplanetary trajectory design problems. Results show that solutions obtained using this tool match known solutions for some complex problems. A comparison between genetic algorithms and differential evolution in the structured-chromosome framework is presented. Nomenclature a = first selected chromosome a = thrust acceleration vector b = second selected chromosome c = third selected chromosome F = fitness (cost function) F 0 = modified fitness (cost function) f = flight direction G = derating function h = pericenter altitude, km h = normalized swingby pericenter altitude iter = iteration m = number of swingby maneuvers NR = number of runs n = number of deep-space maneuvers in a single leg R = mean radius, km r = heliocentric position vector in inertial frame, km r = spacecraft position vector r = spacecraft acceleration vector SC = success counter SR = success rate sol = solution T = time of flight, days t = Julian date tmp = temporary chromosome u = solution feature v ∞ = hyperbolic velocity vector relative to the planet, km∕s w = differential weight ΔV x = x component of impulsive maneuver velocity ΔV y = y component of impulsive maneuver velocity ΔV z = z component of impulsive maneuver velocity Δv = impulsive maneuver velocity vector, km∕s Δv T = total mission cost, km∕s ε = epoch of a deep-space maneuver as a fraction of transfer time η = swingby plane rotation angle, rad μ = gravitational constant, km 3 ∕s 2 Subscripts a = arrival d = departure l = leg number p = swingby's planet ps = powered swingby req = required s∕c = spacecraft T = total Superscripts − = incoming = outgoing

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Automated Interplanetary Mission Planning

Cited by 7 publications

References 33 publications

A multidirectional Physarum solver for the automated design of space trajectories

A multidirectional Physarum solver for the automated design of space trajectories

Automated Solution of the Low-Thrust Interplanetary Trajectory Problem

Structured-Chromosome Evolutionary Algorithms for Variable-Size Autonomous Interplanetary Trajectory Planning Optimization

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