Evolutionary path planning for autonomous air vehicles using multi-resolution path representation

There is a recognized need for automated path planning for unmanned air vehicles (UAVs) and guided munitions. Evolutionary programming approaches provide an alternative to classical functional optimization methods with the capability of incorporating a variety of optimization goals, while tolerating vehicle constraints. In this work, we introduce an evolutionary flight path planning algorithm capable of mapping paths for free-flying vehicles functioning under several aerodynamic constraints. An air-to-ground targeting scenario was selected to demonstrate the algorithm. The task of the path planner was to generate inputs flying a munition to a point where it could fire a projectile to eliminate a ground target. Vehicle flight constraints, path destination, and final orientation were optimized through fitness evaluation and iterative improvement of generations of candidate flight paths. Evolutionary operators comprised of one crossover operation and six mutation operators. Several cases for air-to-ground vehicle targeting have been successfully executed by the evolutionary flight path planning algorithm under challenging initial conditions. A feasible path is typically found rapidly (<100 generations), with further optimization (~3000 generations) insuring a strike very near target center. These results clearly demonstrate that evolutionary optimization using can achieve flight objectives for air vehicles without violating limits of the aircraft.