UAV path planning using evolutionary algorithms

Abstract

Evolutionary Algorithms have been used as a viable candidate to solve path planning problems effectively and provide feasible solutions within a short time. In this work a Radial Basis Functions Artificial Neural Network (RBF-ANN) assisted Differential Evolution (DE) algorithm is used to design an off-line path planner for Unmanned Aerial Vehicles (UAVs) coordinated navigation in known static maritime environments. A number of UAVs are launched from different known initial locations and the issue is to produce 2-D trajectories, with a smooth velocity distribution along each trajectory, aiming at reaching a predetermined target location, while ensuring collision avoidance and satisfying specific route and coordination constraints and objectives. B-Spline curves are used, in order to model both the 2-D trajectories and the velocity distribution along each flight path. © 2007 Springer-Verlag Berlin Heidelberg.