design space pruning heuristics and global optimization method for ...

SUMMARY
Electric propulsion has recently become a viable technology for spacecraft,
enabling shorter flight times, fewer required planetary gravity assists, larger payloads,
and/or smaller launch vehicles. With the maturation of this technology, however, comes a
new set of challenges in the area of trajectory design. Because low-thrust trajectory
optimization has historically required long run-times and significant user-manipulation,
mission design has relied on expert-based knowledge for selecting departure and arrival
dates, times of flight, and/or target bodies and gravitational swing-bys. These choices are
generally based on known configurations that have worked well in previous analyses or
simply on trial and error. At the conceptual design level, however, the ability to explore
the full extent of the design space is imperative to locating the best solutions in terms of
mass and/or flight times.
Beginning in 2005, the Global Trajectory Optimization Competition posed a
series of difficult mission design problems, all requiring low-thrust propulsion and
visiting one or more asteroids. These problems all had large ranges on the continuous
variables – launch date, time of flight, and asteroid stay times (when applicable) – as well
as being characterized by millions or even billions of possible asteroid sequences. Even
with recent advances in low-thrust trajectory optimization, full enumeration of these
problems was not possible within the stringent time limits of the competition.
This investigation develops a systematic methodology for determining a broad
suite of good solutions to the combinatorial, low-thrust, asteroid tour problem. The target
application is for conceptual design, where broad exploration of the design space is
critical, with the goal being to rapidly identify a reasonable number of promising
solutions for future analysis. The proposed methodology has two steps. The first step
applies a three-level heuristic sequence developed from the physics of the problem,
which allows for efficient pruning of the design space. The second phase applies a global
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