Vision-based path planning algorithm of unmanned bird-repelling vehicles in airports

Low-flying birds flying in the vicinity of the airports are a serious threat to the safety of aircraft takeoff and landing, and the existing bird-repelling measurements make it difficult to effectively repel low-flying birds for high instrument resource consumption and large spatio-temporal influence. In order to reduce the workload associated with repelling birds, this paper suggests replacing manned vehicles with unmanned vehicles. These unmanned vehicles will be outfitted with fixed cameras to enable real-time bird detection near the airport, as well as the collection and provision of bird data for the unmanned vehicles’ route planning. The method is divided into two parts: bird detection and path planning of unmanned bird-repelling vehicles. In order to enhance the accuracy of the network’s bird location, this study first addresses bird detection. Specifically, it suggests an enhanced YOLOv5 network that utilizes a coordinate attention mechanism to effectively identify small target birds in real time. Second, in view of the path planning problem of unmanned bird-repelling vehicles, the traditional path planning algorithms need to be improved in perspectives of long path distances and more inflection points. Therefore, an improved beetle swarm optimization algorithm is proposed in this paper, which can effectively shorten the marched distance of unmanned bird-repelling vehicles, accurately avoid static obstacles and dynamic obstacles in the airport, and quickly reach the designated location. The results show that the method can effectively detect airport birds, and provide timely bird data for unmanned bird-repelling vehicles. The route planning distance can be shortened by using the enhanced beetle swarm optimization technique, giving unmanned bird-repelling vehicles quick access to designated locations. It can effectively reduce human resource investments, save the unmanned bird-repelling vehicles energy, and improve the bird-repelling efficiency. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.