Automated mission planning via evolutionary algorithms

Many space mission planning problems may be formulated as hybrid optimal control problems, that is, problems that include both real-valued variables and categorical variables. In orbital mechanics problems, the categorical variables will typically specify the sequence of events that qualitatively describe the trajectory or mission, and the real-valued variables will represent the launch date, flight times between planets, magnitudes and directions of rocket burns, flyby altitudes, etc.Acurrent practice is to preprune the categorical state space to limit the number of possible missions to a number whose cost may reasonably be evaluated. Of course, this risks pruning away the optimal solution. The method to be developed here avoids the need for prepruning by incorporating a new solution approach. The new approach uses nested loops: an outer-loop problem solver that handles the finite dynamics and finds a solution sequence in terms of the categorical variables, and an inner-loop problem solver that finds the optimal trajectory for a given sequence A binary genetic algorithm is used to solve the outer-loop problem, and a cooperative algorithm based on particle swarm optimization and differential evolution is used to solve the inner-loop problem. The hybrid optimal control solver is successfully demonstrated here by reproducing the Galileo and Cassini missions.