Guangdong University of Technology

Renquan Lu

Yong Xu

Hongxia Rao

Jie Tao

Chang Liu

Hui Peng

Wei Meng

Xinyan Li

Zehui Xiao

Kunsong Lin

Yongmin Luo

Hongyi Li

Panshuo Li

Hui Chen

Xinduan Yang

Jiaye Zhou

Yuru Guo

Xiuwei Huang

Zijie Chen

Yuanlin Yang

Ye Kuang

Ming Lin

Jiayao Zang

Jianhong Weng

The present disclosure discloses a method for attitude control of a quadrotor UAV, comprising establishing an attitude dynamics model of the quadrotor UAV, establishing a motion equation and a state-space equation of a UAV control system, determining an LADRC-CFO, and establishing a differential tracker for reducing a system overshoot.

Methods for attitude control of quadrotor unmanned aerial vehicle (UAV)

The present invention relates to the field of intelligent vehicles, and more specifically, to a human-machine shared control method based on trajectory prediction. It comprises: establishing a road-vehicle model based on vehicle-related parameters and then designing a path-tracking controller; further predicting the driver's driving trajectory, evaluating the driving risk, and switching the control power between the automatic system and the human driver based on the driving risk. The present invention ensures that when the driver's inputs will cause high risk to the vehicle, the driver's input is deprived of; and that when the driver's driving inputs will not cause high risk to the vehicle, the driving is carried out by the driver, so that in the case where the automatic system is having a problem, the driver can effectively realize a safe intervention, and the safety of the intelligent driving is improved.

Human-machine shared control based on trajectory prediction

The present invention provides a pose estimation method for an unmanned aerial vehicle based on point, line and plane feature fusion, the method comprises the following steps: S1 extracting an RGB image and a depth map by using an RGB-D camera; S2 constructing a plane parameter space (PPS) according to the depth map, and mapping the depth map from a Cartesian space to the plane parameter space (PPS); S3 constructing an inverse octree structure, comprising: reversely constructing cell nodes for the plane parameter space (PPS) according to an octree rule, and for each cell node, performing fitting by using Gaussian distribution; S4 extracting a plane feature, comprising: extracting the plane feature on the basis of degree of feature extraction algorithm of the inverse octree; S5 extracting a linear feature, comprising: extracting a linear feature on the RGB image by using LSD algorithm detection.

Pose estimation method for unmanned aerial vehicle based on point line and plane feature fusion

The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.

Cloud-edge-end cooperative control method of 5G networked unmanned aerial vehicle for security rescue

A landing tracking control method comprises the following contents: a tracking model training stage and an unmanned aerial vehicle real-time tracking stage. The landing tracking control method extracts a network Snet by using a lightweight feature and makes modification, so that an extraction speed of the feature is increased to better meet a real-time requirement. Weight allocation on the importance of channel information is carried out to differentiate effective features more purposefully and utilize the features, so that the tracking precision is improved. In order to improve a training effect of the network, a loss function of an RPN network is optimized, a regression precision of a target frame is measured by using CIOU, and meanwhile, calculation of classified loss function is adjusted according to CIOU, and a relation between a regression network and classification network is enhanced.

Landing tracking control method and system based on lightweight twin network and unmanned aerial vehicle

A method for synchronizing virtual and real statuses of a digital twin system of an unmanned aerial vehicle (UAV) includes: performing parameter configuration for a virtual object system and a physical object system of the UAV; performing time synchronization between the virtual object system and the physical object system; detecting an event trigger type, wherein the event trigger type is a training event or a monitoring event; and triggering a corresponding synchronization controller based on the detected event trigger type, such that the synchronization controller performs result synchronization and process synchronization for the virtual object system and the physical object system based on the event trigger type, where a synchronization controller corresponding to the training event is a controller for synchronizing a physical object to a virtual object, and a synchronization controller corresponding to the monitoring event is a controller for synchronizing the virtual object to the physical object.

Method and system for synchronizing virtual and real statuses of digital twin system of unmanned aerial vehicle (UAV)

A nearshore real-time positioning and mapping method for an unmanned surface vehicle with multiple distance measuring sensors comprises: acquiring predicted gesture data of the unmanned surface vehicle by an inertia measurement unit; acquiring radar point cloud data by a laser radar, projecting the radar point cloud data to a depth map, and reserving ground points and break points on the depth map; dividing the depth map into six sub depth maps, obtaining a feature point set via a curvature of each laser point, and converting all the feature point sets of the laser radar into coordinates of the unmanned surface vehicle; obtaining a relative gesture transformation matrix of the current unmanned surface vehicle via the radar cloud point data of two adjacent frames; accruing multiple factors, and optimizing a gesture of the unmanned surface vehicle in form of a factor pattern; and constructing a three-dimensional cloud point pattern.

Nearshore real-time positioning and mapping method for unmanned surface vehicle with multiple distance measuring sensors

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Guangzhou, China

Renquan Lu