UAV Smart Navigation: Combining Delaunay Triangulation and the Bat Algorithm for Enhanced Efficiency

In the realm of unmanned aerial vehicles (UAVs), path planning is crucial. The objective is to create a safe, practical, and optimum flight route for UAVs to fly across unfamiliar terrain while avoiding obstacles. Because of their high computational complexity and inability to handle changing surroundings, traditional route planning algorithms confront hurdles in real-world applications. As a result, academics have created many sophisticated ways to handle UAV route planning challenges. This research provides a unique technique for intelligent UAV route planning. The program first divides the research region using the Delaunay Triangulation method. After obtaining the partitions, the coverage points are located, and the issue is framed as the Traveling Salesperson Problem (TSP). The Bat Optimization Algorithm (BOA) is used to find an optimum route for the TSP. The suggested technique seeks to find the best route for UAVs in dynamic situations. The suggested approach was verified by comparing it to other existing algorithms. The findings demonstrate that the suggested method can construct an optimum route that meets all requirements while avoiding obstacles. Moreover, the method may swiftly identify a new route as the environment changes. The study findings show that the suggested approach beats the other techniques in total computing time, saving roughly 0.03 s. Moreover, the suggested technique reduces the UAV’s route by around 0.02 cm, substantially improving existing algorithms.