Generation of Bezier Curve-Based Flyable Trajectories for Multi-UAV Systems with Parallel Genetic Algorithm

In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many military and civil application areas, due to their increased endurance, performance, portability, and their larger payload-carrying, computing and communication capabilities. Because of UAVs' complex operation areas and complicated constraints related to the assigned task, they have to fly on a path, which is calculated online and/or offline to satisfy these constraints and to check some control points in the operation theatre. If the number of control points and constraints increases, finding a feasible solution takes up too much time in this large operation area. In this case, the use of multi-UAVs decreases operation completion time; however, this usage increases the complexity of finding a feasible path problem. This problem is typically NP-hard and genetic algorithms have been successfully utilized for solving it in the last few decades. This paper presents how a flyable trajectory can be constructed for multi-UAV systems by using a Genetic Algorithm (GA) in a known environment and at a constant altitude. A GA is implemented parallel in a multi-core environment to increase the performance of the system. First, a feasible path is calculated by using a parallel GA, and then the path is smoothed by using Bezier curves to convert it flyable. Preliminary results show that the proposed method provides an effective and feasible path for each UAV in an Unmanned Aerial System with multi-UAVs. The proposed system is realized in Java with a GUI for showing results. This paper also outlines future work that can be conducted on the multi-UAV path planning.