optimizes satellite orbits
Technology Research News
Plotting orbits for a constellation of
satellites is a complicated business.
Orbiting closer to earth makes it possible for a satellite to communicate
with low-power devices like cellphones, but doing so produces more blind
spots, because to maintain contact a satellite must have a line of sight
to earthbound antennas.
Researchers from Purdue have used genetic
algorithms to design orbits for constellations of satellites that
are more efficient than current group orbits. Conventional orbits for
constellations of three or four low-altitude satellites that circle the
earth in about 90 minutes can cover most of the earth for most of the
day, but have blackout periods.
Satellite orbits are tricky to plan because they have two objectives:
to shorten both the longest gap in coverage and the average time a gap
will last, said William Crossley, an associate professor of aeronautics
and astronautics at Purdue University.
The trouble is, shortening one type of gap often widens the other. "The
optimization routine [that] concentrates on reducing the coverage gaps
that are the longest in duration... does not consider the average revisit
time experienced by all ground stations. Conversely, designing a constellation
to minimize [average coverage gaps] will reduce the revisit time for most
points in the area of interest on the earth, but this may allow some locations
to have very long gaps in coverage," said Crossley.
The researchers found a more efficient orbit using a type of genetic algorithm
that can handle two objectives. Genetic algorithms are based on the Darwinian
model of natural selection, or survival of the fittest. "In the computer,
a population of possible satellite constellations is represented by a
chromosome," said Crossley.
The researchers' satellite chromosome included three types of orbit information:
the orbit's angle to the equator, the longitude where the satellite orbit
crosses the equator, and how far from the equator each satellite is when
the first satellite in the constellation crosses the equator. Each constellation
also had a fitness value depending on the efficiency of its combination
The genetic algorithm mixed the chromosomes of the most fit individual
constellations to make a new generation of constellations, and the fittest
ones went on to produce new generations. "The survival of the fittest
behavior acts to improve designs over numerous generations," Crossley
The researchers ran the satellite algorithm for 200 generations in order
to generate large numbers of solutions mapping the trade-offs in minimizing
the two types of gaps. "We conducted these investigations for different
numbers of satellites in different altitudes, and the genetic algorithm
generated between 10 and... 30... designs representing the trade-offs,"
An interesting pattern emerged. "We found that for small numbers of satellites,
the constellations with low [gaps] were often nonsymmetric," meaning that
there are nonuniform intervals between the satellites' equator longitude
and equator distance values, Crossley said. "The nonsymmetric constellation
results were fairly surprising, mostly because traditional [orbit mapping
methods] relied heavily upon symmetric orbits," he said.
Crossley is applying the approach to other areas, while his research colleagues
at the Aerospace Corporation are using the satellite data to find practical
constellations, Crossley said. In general, the approach can "help engineers
and designers search through a complex design space and find good, possibly
nonintuitive solutions to aerospace problems," he said.
The research does a good job of applying multi-objective genetic algorithm
methods to an interesting problem, said Erik Goodman, a professor of electrical
and computer engineering, and mechanical engineering at Michigan State
University, and vice president of technology at Applied Computational
Design Associates, Inc. "This sort of planning, and particularly the explicit
presentation of the trade-offs between the two objectives... is difficult
to do" using conventional methods, and is therefore a good application
for this type of genetic algorithm, he said.
Crossley's research colleagues were Edwin A. Williams of Purdue University
and Thomas J. Lang of the Aerospace Corporation, a nonprofit entity that
provides science and engineering consulting services to the U.S. Air Force
and other U.S. government entities.
The research was funded by the Aerospace Corporation.
TRN Categories: Artificial Life and Evolutionary Computing;
Story Type: News
Related Elements: Related technical paper, "Average and
Maximum Revisit Time Trade Studies for Satellite Constellations Using
a Multiobjective Genetic Algorithm," AAS/AIAA Space Flight Mechanics Meeting
in Clearwater, Florida January 23-26, 2000.
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