Target Tracking and Navigation for Intelligent Autonomous Unmanned Systems Application
Versandkostenfrei!
Versandfertig in 1-2 Wochen
63,99 €
inkl. MwSt.
PAYBACK Punkte
32 °P sammeln!
The development of artificial intelligence technology can enhance the capability of autonomous unmanned systems and form intelligent autonomous unmanned systems (iAUSs). In this issue, including 10 papers, intelligent technologies applied to air/ground/underground traffic and so on are discussed. In the field of UAV application, here shows a high resolution UAV image traffic sign detection method based on a visual language model, a real-time trajectory smoothing obstacle avoidance method based on virtual force guidance, a vision inertial RGB-D SLAM system based on the encoder integrated uncert...
The development of artificial intelligence technology can enhance the capability of autonomous unmanned systems and form intelligent autonomous unmanned systems (iAUSs). In this issue, including 10 papers, intelligent technologies applied to air/ground/underground traffic and so on are discussed. In the field of UAV application, here shows a high resolution UAV image traffic sign detection method based on a visual language model, a real-time trajectory smoothing obstacle avoidance method based on virtual force guidance, a vision inertial RGB-D SLAM system based on the encoder integrated uncertainty of ORB triangulation and depth measurement.In the field of ground traffic intelligent transportation, here shows some methods: an end-to-end synchronous vehicle pedestrian detection algorithm based on improved YOLOv8, a new multi-scale track surface defect detection model (RSDNet), a transmission line defect detection method based on GR-YOLOv8, a fusion algorithm based on double domain transform filtering and contrast transform feature extraction.In the field of underground roadway robots, the obstacle avoidance trajectory planning method of coal mine drilling, and the anchoring mechanical arm transporting drilling and anchoring materials are studied. A time series prediction method based on optimized variational mode decomposition and SSA-LSTM is proposed.
Andere Kunden interessierten sich für
