Citation: Sankaran, K.; Griffith, S.A.;
Thompson, N.C.; Lochridge, M.D.;
O’Kins, A.S. A Parallelized Genetic
Algorithm to Evaluate Asteroid
Impact Missions Using Electric
Propulsion. Aerospace 2022, 9, 116.
https://doi.org/10.3390/
aerospace9030116
Academic Editors: Mikhail
Ovchinnikov and Dmitry Roldugin
Received: 6 January 2022
Accepted: 21 February 2022
Published: 24 February 2022
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Article
A Parallelized Genetic Algorithm to Evaluate Asteroid Impact
Missions Using Electric Propulsion
Kamesh Sankaran
1,
* , Scott A. Griffith
2
, Noah C. Thompson
1
, Matthew D. Lochridge
1
and Andrew S. O’Kins
2
1
Department of Engineering & Physics, Whitworth University, Spokane, WA 99251, USA;
nthompson22@my.whitworth.edu (N.C.T.); mlochridge22@my.whitworth.edu (M.D.L.)
2
Department of Mathematics & Computer Science, Whitworth University, Spokane, WA 99251, USA;
sgriffith@whitworth.edu (S.A.G.); aokins22@my.whitworth.edu (A.S.O.)
* Correspondence: ksankaran@whitworth.edu
Abstract:
A streamlined genetic algorithm was developed and implemented on a GPU to evaluate
low-thrust trajectories of spacecraft propelled by an ion thruster. It was then applied to examine the
utility of a specific thruster for an asteroid impact mission. This method was validated by comparing
impact speeds of non-thruster results with the DART mission, which does not significantly use
the equipped ion thruster. Then, by utilizing the ion thruster for prolonged periods, this model
demonstrated the possibility of significant increases in the impact speed and significant decreases in
the trip times. This specific test case was used to examine the utility of the model and, by methodically
varying relevant variables, this article shows the influence of the genetic algorithm on the results.
By examining a range of electrical power levels, the results presented here provide hints as to the
possible effects of spacecraft design trade-offs on impact speed. The analysis of the effects of the
algorithm on the results and the evaluation of thruster operating parameters indicate the applicability
of this model to a variety of spaceflight missions.
Keywords: electric propulsion; asteroid mission; genetic algorithm
1. Introduction
Missions to asteroids using electric propulsion have been studied for various destina-
tions and thruster options [
1
–
7
]. Asteroid impact missions, such as the Double Asteroid
Redirection Test (DART) [
8
], employ a kinetic impactor to change the orbit of an asteroid.
Various publications [
9
–
12
] have evaluated the use of the NASA Evolutionary Xenon
Thruster (NEXT) [
13
] to evaluate design options for the DART mission. The DART mission
was launched on 24 November 2021 to impact the Didymos asteroid system 430 days
later with an impact speed of 6.58 km/s. Ozimek and Atchison developed a low-thrust
trajectory concept using the NEXT thruster if the DART mission was to be launched as a
ride-share from a geostationary transfer orbit (GTO) [
11
]. In the same vein, Sarli et al. [
12
]
evaluated its cruise phase using the NEXT-Commercial (NEXT-C) thruster. Though the
DART mission has abandoned the commercial ride-share approach in favor of a designated
launch vehicle that has put the spacecraft directly on an earth-escape trajectory, Refs. [
11
,
12
]
provide a starting point for our work to demonstrate an alternative method for analyzing
such missions.
The objective of this article is to demonstrate the utility of a parallelized baseline
genetic algorithm by evaluating the effect of a specific thruster on an asteroid impact
mission. We validated our algorithm by comparing its results to the DART mission,
whose relevant details are provided in Sections 4.1 and 4.2. Genetic algorithms have been
used extensively to optimize spacecraft trajectories for various missions [
14
–
17
]. Among
methods that calculate differential equations of motion consistently, genetic algorithms offer
an advantage of using diversity in the parameter space to dynamically tune solutions to
optimize the trajectory and avoid local minima. However, genetic algorithms are inefficient
Aerospace 2022, 9, 116. https://doi.org/10.3390/aerospace9030116 https://www.mdpi.com/journal/aerospace
