摘要
机场飞行区存在的低空飞鸟严重威胁飞行器的起飞和降落安全,现有的驱鸟措施难以高效驱离低空飞鸟,且存在设备资源消耗高、受时空影响大等问题。为此,使用无人驱鸟车替代有人驾驶车辆进行驱鸟工作,并使用搭载固定摄像云台的无人驱鸟车对机场低空中鸟类进行实时检测,获取鸟情数据后,为无人驱鸟车路径规划提供鸟情数据基础。针对鸟类检测的问题,提出一种基于坐标注意力机制改进的YOLOv5网络,对小目标鸟类进行高效的实时检测,使网络更加精准地对鸟类进行定位;针对传统路径规划算法存在路径距离较长、拐点较多等缺陷,提出一种改进的天牛群算法,可有效缩短无人驱鸟车行驶距离,精准躲避机场内静态障碍物和动态障碍物,并快速到达指定驱鸟位置。实验结果表明:所提算法可对机场鸟类进行有效检测,为无人驱鸟车及时提供鸟情数据,利用改进的天牛群算法缩短规划路径的距离,使无人驱鸟车更加精准快速地到达指定驱鸟位置,有效减少人力资源投入,节约无人驱鸟车行进所需能源,提高驱鸟效率。
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.
作者
王蕊
李金洺
史玉龙
孙辉
WANG Rui;LI Jinming;SHI Yulong;SUN Hui(College of Information Engineering and Automation,Civil Aviation University of China,Tianjin 300300,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2024年第5期1446-1453,共8页
Journal of Beijing University of Aeronautics and Astronautics
关键词
卷积神经网络
鸟类检测
无人车
天牛群算法
路径规划
convolutional neural networks
bird detections
unmanned ground vehicle
beetle swarm optimization algorithm
path planning
