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Evolutionary Computing for Low-thrust NavigationThe development of new mission concepts requires efficient methodologies to analyze, design and simulate the concepts before implementation. New mission concepts are increasingly considering the use of ion thrusters for fuel-efficient navigation in deep space. This paper presents parallel, evolutionary computing methods to design trajectories of spacecraft propelled by ion thrusters and to assess the trade-off between delivered payload mass and required flight time. The developed methods utilize a distributed computing environment in order to speed up computation, and use evolutionary algorithms to find globally Pareto-optimal solutions. The methods are coupled with two main traditional trajectory design approaches, which are called direct and indirect. In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. In the indirect approach, a thrust control problem is transformed into a costate control problem, and the initial values of the costate vector are optimized. The developed methods are applied to two problems: 1) an orbit transfer around the Earth and 2) a transfer between two distance retrograde orbits around Europa, the closest to Jupiter of the icy Galilean moons. The optimal solutions found with the present methods are comparable to other state-of-the-art trajectory optimizers and to analytical approximations for optimal transfers, while the required computational time is several orders of magnitude shorter than other optimizers thanks to an intelligent design of control vector discretization, advanced algorithmic parameterization, and parallel computing.
Document ID
20060050337
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Lee, Seungwon
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Fink, Wolfgang
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
vonAllmed, Paul
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Petropoulos, Anastassios E.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Russell, Ryan P.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Terrile, Richard J.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Subject Category
Mathematical And Computer Sciences (General)
Meeting Information
Meeting: AIAA Space Conference, Long Beach, California, August 31 - September 01, 2005
Location: Long Beach, CA
Country: United States
Start Date: August 31, 2005
End Date: September 1, 2005
Distribution Limits
Public
Copyright
Other
Keywords
optimization
low thrust navigations
evolutionary computing

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