Environment Perception And Path Planning For Driverless On Unstructured Roads

With the rapid development of information technology and its wide application in the military field,military intelligent vehicle unmanned operations will be the main military means for the army to complete special operations tasks,implement precise fire strikes and reduce casualties in mobile warfare,nuclear warfare,biological warfare and other harsh battlefields in the future,and it will be one of the key technical fields of national defense and military research in the future.In this paper,the unstructured road environment is taken as the research scene,and the driving area segmentation,obstacle target detection and path planning technologies of unstructured road are deeply studied,so as to improve the flexibility and maneuverability of unmanned combat in complex environment.The research work of this paper mainly includes the following aspects:Firstly,based on the self-built unstructured road scene data set,the accuracy segmentation problem of unstructured road in complex environment is studied,and an Attention Transformer Deeplabv3+(ATD)semantic segmentation algorithm for unstructured road is proposed.The convolutional attention CBAM module is combined in the intermediate level of encoding feature extraction,which enhances the adaptive pixel weights of semantic information in different channels and spatial dimensions,and strengthens the feature coding ability in complex environments.Transformer introduces Multi-Head Attention in decoding to strengthen the relevance of spatial location information and realize the fine-grained reasoning of unstructured road edges.Secondly,aiming at the problems of missing detection,false detection,low detection and recognition accuracy in the complex unstructured road scene,a multiscale obstacle target detection algorithm YOLOv5＿Head is proposed based on road segmentation in the driveable area.Experiments show that the obstacle target detection algorithm proposed in this paper can effectively improve the detection and classification ability of targets of different scales,reduce the occurrence of missed detection and false detection in close-range scenes,distant scenes and dense scenes,and enhance the detection ability of distant small targets.Finally,in view of the complexity of unstructured roads and the variability of the environment,the task research of path planning is conducted based on the environment perception information of unstructured roads.The experiment shows that RRT path planning algorithm can effectively plan a crash-free safe driving trajectory from the starting point to the target point under the complex unstructured road environment.It improves the maneuverability and flexibility of military intelligent vehicles in unmanned combat in harsh battlefield environment.