TY - GEN

T1 - Optimal autonomous mission planning via evolutionary algorithms

AU - Englander, Jacob A.

AU - Conway, Bruce A

AU - Williams, Trevor

PY - 2011/10/6

Y1 - 2011/10/6

N2 - Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. 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. A current practice is to pre-prune 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 pre-pruning 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 HOCP solver is successfully demonstrated here by reproducing the Galileo and Cassini missions.

AB - Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. 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. A current practice is to pre-prune 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 pre-pruning 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 HOCP solver is successfully demonstrated here by reproducing the Galileo and Cassini missions.

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M3 - Conference contribution

AN - SCOPUS:80053431387

SN - 9780877035695

T3 - Advances in the Astronautical Sciences

SP - 833

EP - 852

BT - Spaceflight Mechanics 2011 - Advances in the Astronautical Sciences

T2 - 21st AAS/AIAA Space Flight Mechanics Meeting

Y2 - 13 February 2011 through 17 February 2011

ER -