Optimal flight path planner for an unmanned helicopter by evolutionary algorithms

This paper presents an evolutionary method to develop an optimal flight path planner for an unmanned helicopter with initial, final states, and waypoint constraints under certain prescribed operational environment. The operational environment consists of concave and non-concave obstacles which are represented by different geometric shapes and their combinations. The minimum flight time is considered as the objective function in the optimization process. The stochastic universal sampling selection technique, mutation and crossover operators are implemented in the evolutionary method. Finally, the method is validated by applying to optimal flight path problems in highly constrained operational environments.