Spacecraft
could soon take advantage of a sophisticated math algorithm that simulates
evolution to find the best paths to distant planets and comets.
Engineers
at the University of Missouri tweaked a mathematical approach called "differential
evolution" so that it works quickly and efficiently to plot the best course
for robotic deep space missions.
"This
helps you figure out trajectory, size up the spacecraft, how much fuel is needed,
what kind of launch vehicles are needed ... all answers you need to get before
going into the mission details," said Craig Kluever, aerospace engineer at
the University of Missouri.
The math algorithm
treats possible solutions as individuals in a population, choosing a few each
time to "mutate" and swap traits, then testing the mutants against
the previous solutions. The best solutions win out and survive to the next
generation, where the process may repeat again and again.
Applying
this approach to calculating spacecraft trajectories is "not new, but it's
catching on," said Aaron
Olds, a former MU grad
student who worked with Kluever. The European Space Agency (ESA) sponsored two
studies that compared differential evolution with other methods — one study
deemed differential evolution the best, while the other study found its
performance just average.
This
contradiction in success arose because the ESA researchers used different
numbers for population size, rate of mutation and the likelihood of traits
crossing over between solutions. Kluever and Olds set out to find the best
numbers for calculating spacecraft trajectories.
They
fine-tuned the algorithm by testing it in a software program against four space
mission scenarios — including the complex 1997 Cassini mission
to Saturn that involved swing-bys of Earth, Venus and Jupiter, as well as deep
space maneuvers.
"The
Cassini results were actually very close to what was actually flown,"
noted Kluever. "A lot of event times and flybys were right on the same day
or just off by one day."
Many of the
best solutions for Cassini did not precisely happen during the mission because
of real world constraints. For instance, a planned course correction might have
been delayed because mission control had problems communicating with the
Cassini spacecraft.
Such real
world constraints will play a role in any real missions, but the differential
evolution algorithm simply ignores them. Kluever and Olds think the approach
can best help mission planners who design challenging future missions to distant
targets within the solar system.
Olds
pointed to recent "missions that require a little more computational power,"
such as the International Rosetta mission that will chase down a comet and put
a lander on the surface by 2014. Rosetta's complex trajectory has already
included two
swing-bys of Earth and one of Mars, with a final Earth swing-by planned in
2009 before the spacecraft heads for its final destination.
The differential
evolution approach could also apply to future missions such as a crewed mission
to Mars, which Kluever and Olds used as a scenario to fine-tune the
algorithm.
Mission planners currently use a variety of
tools, including a "design driven" approach where experienced analysts make a best
guess for spacecraft trajectories before making calculations, Olds said. He and
Kluever hope that space agencies will continue looking into differential
evolution.
"I
think it'd be nice if NASA would like to put it in their toolbox," said
Kluever. "It's not going to be a replacement, but you can look at a
problem from a different angle."
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