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

Abstract

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. The results demonstrate that evolutionary optimization can achieve flight objectives for air vehicles without violating limits of the aircraft.