UAV Path Planning based on Road Extraction

With the development of science and technology, UAVs are increasingly being used and serving humans, especially in the wilderness environment, due to their portability and the ease with which they can reach places that are beyond human reach. In this paper, we present a technique for drones to help humans intelligently plan routes in a field environment. Our approach is firstly based on road extraction techniques in the field of image segmentation, using state-of-the-art D-LinkNet to extract roads from images captured by real-time UAVs. Secondly, the extracted road information is analyzed, the set of main roads and that of the secondary road are distinguished according to the width and the real-time road conditions on the ground, and different weights are assigned to them. Finally, the A star algorithm is used to calculate a route plan with weights based on the human-defined starting and ending points to obtain the optimal route. The results of our task are the simulations on publicly available datasets to show that the method works well to provide the optimal intelligent routes in real-time for people in the field.