An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems

Abstract

Unmanned aerial vehicles (UAV) have become an important and integral part of military and civilian operations in recent years. In many UAV missions, the main purpose is to visit some predetermined checkpoints in operational space. If the number of checkpoints and constraints increases, finding a feasible solution may take up too much time. In this paper; the path planning problem of autonomous UAV in target coverage problems is solved by using artificial intelligent methods including genetic algorithm (GA), ant colony optimizer (ACO), Voronoi diagram, and clustering methods. The main contribution of this article is to propose initial population enhancement methods in GA, and thus accelerate convergence process. The first common enhancement to basic GA structure is to generate a sub-optimal path by implementing ACO. A sub-optimal path can be used to generate initial individuals. However, sub-optimal paths may have the problem that is collision with terrain. To avoid a UAV from any crash three approaches are integrated into an initial population phase of genetic algorithm. The first approach includes Voronoi vertices as additional waypoints to keep clear of trouble. The second approach consists of cluster centers which forms Voronoi vertices as supplemental waypoints. The final proposal comprises again cluster centers but based on a set of collision points. The proposed methods are tested in different three dimensional (3D) environments and the results are compared. Performance results show that collision with terrain surface is a local phenomenon and solving this issue by using the cluster center of collision points provides the best result including at least 70% or much more decrease in the required number of objective function evaluations.