Rendezvous trajectory optimization using real genetic algorithm combined with gradient method

Abstract

Although direct and indirect methods have been widely applied to trajectory optimization problems, optimization results for these methods are sensitive to initial solutions in some cases. For the purpose of finding an appropriate initial solution of rendezvous problem to calculus-of-variations-based trajectory optimization, a numerical trajectory optimization method using a real-coded genetic algorithm is considered. The genetic algorithms are not hampered by ill-behaved gradients and are relatively insensitive to problems with a small radius of convergence. Those have been successfully applied to numerical optimization problems. The use of calculus of variations within the genetic algorithm optimization routine increases the precision of the final solution to levels uncommon for a genetic algorithm alone.