22results about How to "Implement tracking" patented technology

The invention belongs to the technical field of robot teleoperation and haptic interaction, and provides a multi-mode force feedback control method and system for nuclear facility robot operation, and the method comprises the steps: collecting the state information of a master arm and a slave arm in real time, and dynamically scheduling a haptic calculation strategy according to a task state; a fast estimation strategy is enabled in the fast transit mode. Further, real environment interaction force is obtained based on dynamic model compensation and net interaction force extraction; combining the dynamically selected multi-mode force feedback parameters, and synthesizing and outputting a touch instruction to the main arm. Meanwhile, admittance correction and feed-forward compensation are carried out on a slave arm motion instruction through net interaction force and dynamic internal force, synchronous output is carried out after safety optimization, and the slave arm is controlled to move. The problems of poor task adaptability, force sense distortion and insufficient safety in nuclear facility teleoperation are solved, and intelligent, high-fidelity and safe robot teleoperation is achieved.

The application provides a kind of indoor global vision-based unmanned vehicle positioning method and device, the method comprises: step 1, visual sensor is fixed on indoor ceiling, and the ground environment image of the periphery of mobile robot in running is collected;Two two-dimensional code labels are arranged on the top of the mobile robot, and the mobile robot is identified by two two-dimensional codes;Step 2, the ground environment image obtained in step 1 is transformed by image processor Abnormal image is obtained by distortion correction;Step 3, the normal image is extracted to obtain feature points;More than two normal images with feature points captured by different vision sensors are matched;Step 4, the image is fused to obtain the wide-area panoramic image after splicing;Step 5, the pose information of the mobile robot is solved.The application can realize the control and tracking of mobile robot in wide-area complex indoor environment, and realize the accurate navigation of mobile robot.

The invention belongs to the technical field of inter-satellite laser link establishment pointing, and provides an inter-satellite laser link establishment pointing method and system suitable for a low-orbit mobile communication constellation in order to solve the problem that an existing azimuth angle and pitch angle pointing technology based on an east-north-sky coordinate system has defects. According to an inter-satellite laser communication ephemeris mutual transmission mechanism, a mathematical model of inter-satellite laser link establishment angle pointing requirements is established, and azimuth angle and pitch angle calculation formulas of laser link establishment of two adjacent satellites in a constellation orbital plane, satellites in the orbital plane and satellites in two adjacent orbital planes are deduced. On one hand, by introducing an orbital coordinate system, an inter-satellite azimuth angle and a pitch angle are defined; on the other hand, a mathematical model of an inter-satellite laser link establishment pointing angle is obtained based on a positioning orbit determination ephemeris data source of an on-satellite GPS. And the inter-satellite laser link establishment pointing technology can be realized through a two-dimensional rotating mechanism of the laser. The method can be widely applied to an inter-satellite laser link establishment pointing scene of a low-orbit mobile communication constellation.

This invention provides a control method for dual-motor driven tracked vehicles based on a neural network prediction model. By introducing a Long Short-Term Memory (LSTM) neural network framework, it trains a neural network prediction model for the tracked unmanned platform, fully leveraging the LSTM's ability to process sequential data. This allows for the extraction of vehicle dynamics features from past driving data to predict the state at the next moment. This method can comprehensively control lateral displacement deviation, heading angle deviation, longitudinal velocity, and yaw rate, as well as track the desired trajectory position, while also ensuring steering controllability. Furthermore, this invention provides a trajectory tracking control method based on Neural Network Model Predictive Control (NN-MPC), simultaneously considering the vehicle's kinematics and dynamics, effectively overcoming the problem of poor trajectory tracking performance caused by the strong coupling of lateral and longitudinal dynamics in the physical model MPC method.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The application belongs to the technical field of image recognition segmentation, and discloses a medical image cell segmentation and tracking method, which comprises the following steps: step 1: data processing; feature extraction: the backbone part in the model is used to extract features from the preprocessed image, and the CSPDarknet structure is adopted in YOLOv8; step 3: FPN-PAN multi-scale feature fusion; step 4: Head prediction according to multi-scale features. The application realizes real-time tracking of the motion trajectory of cells by combining with a tracking algorithm such as deepsort. The method is mainly based on the YOLOv8 framework, and the Simam attention mechanism and the multi-scale proto method are adopted to optimize the model, so that the detection effect of YOLOv8 is further improved. The application can automatically complete the analysis and detection of medical images, is high in convenience and easy to use.

The application provides a signal timing and networked automatic driving vehicle speed hierarchical collaborative control method based on reinforcement learning, and relates to the technical field of intelligent traffic control and vehicle-road collaborative control, and comprises the following steps: obtaining the stop-and-drive vehicle state information on the import lane served by each phase at each signal cycle boundary, and generating a reference timing scheme of the next cycle by a cycle timing intelligent agent based on the stop-and-drive vehicle state information; obtaining a refined discharge window; and outputting a continuous longitudinal acceleration control instruction to a target networked automatic driving vehicle by a networked automatic driving vehicle speed intelligent agent according to the refined discharge window, so that the arrival time of the target networked automatic driving vehicle matches the discharge window, and the collaborative control of traffic signal timing and vehicle speed is realized.

The application discloses a multi-target group tracking method based on an ellipse model with angular parameters and variational inference, and comprises the following steps: determining a state parameter prediction value of a current moment according to a joint probability density of state parameters of a target group of a previous moment, wherein extended shape parameters in the state parameters of the target group are described according to an ellipse model with angular parameters; filtering clutter according to the prediction value of the state parameters of the current moment to determine effective measurements; and determining a joint probability density of the state parameters of the current moment based on variational inference according to the prediction value of the state parameters of the current moment and a related measurement of the target group determined according to the effective measurements. According to the method provided in the application, the extended shape of the target group is described by using the ellipse model with angular parameters, the background clutter is filtered, and the state parameters of the target group of a next moment are approximately estimated based on the method of variational inference, so that the direction angle of the target group can be tracked, the estimation collapse problem caused by excessively large extended shape variance can be avoided, and the tracking accuracy is improved.

The application belongs to the technical field of target tracking, and particularly relates to a target tracking method based on fusion of vision and radar point clouds, wherein through the use of perspective projection, three-dimensional LiDAR points are projected to the coordinate plane of a two-dimensional image to obtain the two-dimensional features P of the point cloud; a double-flow residual network is designed, the aligned point cloud and image are respectively subjected to instance segmentation by using a point cloud center segmentation method and a 2D image segmentation method, the segmented instances are fused, and two independent MLPs are introduced to perform instance recognition and calibration reference frame. Finally, a double-mode instance correction module is input to correct the instance label. In the target tracking module, the weighted center of the instance is used for inter-frame tracking target image matching and trajectory management, so that the unmanned aerial vehicle can detect, locate and track the target of interest and the environmental target, the problems in single-mode recognition and positioning and other problems are alleviated, and the performance of multi-target visual positioning and tracking is improved.

The application discloses a time-space evolution analysis method and system for muzzle electric arc energy and centroid, relates to the technical field of muzzle electric arc analysis, and comprises the following steps: building an electromagnetic track launching muzzle electric arc simulation experiment platform, acquiring a muzzle electric arc image by using a high-speed photography system, obtaining an electric arc contour image based on a local adaptive threshold segmentation and a morphological gradient extraction algorithm, and improving the integrity and robustness of electric arc contour extraction; based on a robust centroid tracking algorithm of multi-connected energy fusion and time sequence consistency constraint, obtaining a final electric arc centroid, realizing stable positioning and tracking of the electric arc energy centroid in the whole evolution stage; and based on the muzzle electric arc images under different muzzle velocities, quantitatively analyzing the time-domain evolution characteristics of the high-light area of the muzzle electric arc and the law of the spatial motion trajectory of the centroid. The application provides an effective means for quantitative characterization of the muzzle electric arc, and provides experimental basis and theoretical reference for muzzle structure optimization, anti-ablation design and launch working condition regulation of an electromagnetic launching system.

This invention discloses a three-stage UAV terminal flight guidance and tracking system and method, relating to the field of UAV technology. It includes an analog camera module, a digital-to-analog converter chip module, a computing chip module, an OSD chip module, a flight controller module, a data transmission module, an analog image transmission module, a remote controller module, and an FPV glasses module. Combining current technological and human resources conditions, this invention uses an FPV UAV target detection and tracking module as an anchor point to construct an independent and controllable integrated hardware and software UAV flight guidance system. This system possesses high robustness, high precision, and high synergy with the flight control system, effectively filling existing technological gaps, meeting the advanced needs of various industries for intelligent UAV flight guidance operations, promoting the popularization and upgrading of UAV flight guidance technology, and laying a solid foundation for subsequent intelligent UAV flight guidance product projects. It has significant technological value and promising industrial application prospects.

This invention relates to a method for analyzing the behavior of stored grain pests based on YOLOv5 and an improved SORT algorithm, belonging to the technical field of stored grain pest behavior analysis methods. This method utilizes a camera to collect and filter videos of stored grain pests crawling, and processes the videos frame by frame. First, each frame is preprocessed using OpenCV to detect the outline of the stored grain pests and extract target coordinate data. When target overlap or loss occurs in a frame, resulting in detection failures for certain stored grain pests, the frame is segmented and fed into the YOLOv5 target detection network for re-detection, optimizing the detection results. This method solves the problems of low accuracy, susceptibility to environmental factors, and poor detection performance when overlap occurs in existing stored grain pest behavior analysis methods, making it particularly suitable for the needs of stored grain pest behavior analysis.

The application discloses a laser positioning method based on a bright frame image. An execution subject of the laser positioning method is a robot equipped with a structured light module. The structured light module comprises a line laser emitter and a camera. The laser positioning method comprises the following steps: the robot searches for a line laser position from the bright frame image by executing an inter-frame tracking algorithm, and sets a coordinate of the line laser position as a positioning coordinate of the line laser emitted by the line laser emitter in a current frame image.

The invention provides an unmanned aerial vehicle cluster time-varying formation tracking control method and device based on an RBF neural network, and a storage medium, so as to effectively fit the external input of a leader unmanned aerial vehicle, thereby achieving better time-varying formation tracking control precision. The method comprises the following steps: constructing an expected time-varying formation configuration instruction of a follower unmanned aerial vehicle in an unmanned aerial vehicle cluster; dynamic models of the leader unmanned aerial vehicle and the follower unmanned aerial vehicle are established; constructing a communication topological relation between the leader unmanned aerial vehicle and the follower unmanned aerial vehicle, and calculating a characteristic value of a communication topological matrix of the follower unmanned aerial vehicle; controller parameters are obtained according to the characteristic values of the communication topology matrix and the dynamical model; periodically iteratively calculating a control instruction of the follower unmanned aerial vehicle based on an RBF neural network, the follower unmanned aerial vehicle expected time-varying formation configuration instruction and controller parameters; and performing formation tracking control based on the control instruction.

The invention relates to a Buck photovoltaic optimizer circuit and a control method, and aims at transmitting electric energy to an energy storage device through a Buck power circuit by a photovoltaic cell, quantizing ambient light intensity changes according to changes of output voltage of the photovoltaic cell, distinguishing extremely weak light, low light, constant light and strong light, and adjusting duty ratio signals in cooperation with a control circuit. In the Buck photovoltaic optimizer circuit, a control signal is generated, a corresponding driving voltage signal is generated through the gate driving circuit to drive the Buck power circuit, accurate switching of the Buck photovoltaic optimizer circuit about a forbidden mode, a standby mode, an MPPT mode and a straight-through mode is achieved, in the MPPT mode, the control circuit dynamically adjusts a duty ratio signal based on the single-peak characteristics of the voltage and power of a photovoltaic cell, and in the straight-through mode, the maximum power of the photovoltaic cell is adjusted. It is ensured that the photovoltaic cell continuously works at the maximum power point, tracking of the maximum power point is achieved, and operation and maintenance efficiency and equipment safety are ensured.

The invention discloses a gesture tracking method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the time synchronization of an obtained hand motion data set and an obtained electromyographic signal data set based on a preset audio signal, and obtaining the synchronized hand motion data set and the synchronized electromyographic signal data set; associating the hand action data and the electromyographic signal data at the same moment in the synchronized hand action data set and the electromyographic signal data set to construct a plurality of training samples; according to each training sample, performing iterative training on a preset to-be-trained model to obtain a gesture tracking model; and tracking the gesture of the target user based on the gesture tracking model. According to the method and the device, the natural interaction experience between the user and the equipment can be improved while the gesture tracking precision is ensured.

The utility model discloses a transmission mechanism for a photovoltaic panel, which comprises a driving shaft and a clutch mechanism, the clutch mechanism comprises a fixed part and a moving part which are oppositely arranged, and the fixed part of the clutch mechanism is fixedly connected with the output end of the driving shaft; under the action of external force, the moving part can move relative to the fixed part, so that the clutch state of the clutch mechanism is switched; when the clutch mechanism is in a connection state, the rotation of the driving shaft can drive a fixed part and a moving part of the clutch mechanism to synchronously rotate; and when the clutch mechanism is in a separated state, the rotation of the driving shaft can drive the fixed part of the clutch mechanism to rotate relative to the moving part. Synchronous rotation of the first load and the second load can be achieved, relative rotation between the first load and the second load can also be achieved, and therefore the folding function is achieved. The utility model also discloses a photovoltaic panel assembly and a photovoltaic device.

The invention discloses a gesture tracking method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the time synchronization of an obtained hand motion data set and an obtained electromyographic signal data set based on at least one reference timestamp of synchronization equipment, obtaining a synchronized hand action data set and an electromyographic signal data set; associating the hand action data and the electromyographic signal data at the same moment in the synchronized hand action data set and the electromyographic signal data set to construct a plurality of training samples; according to each training sample, performing iterative training on a preset to-be-trained model to obtain a gesture tracking model; and tracking the gesture of the target user based on the gesture tracking model. According to the method and the device, the natural interaction experience between the user and the equipment can be improved while the gesture tracking precision is ensured.

This invention discloses a cross-camera face tracking method based on spatiotemporal correlation. By adopting the idea of ​​face tracking, this method effectively reduces the number of face recognition operations, thereby reducing the time and resource overhead of face feature extraction and face comparison in a massive face database. Utilizing the relatively fixed position of a face relative to the overall pedestrian population, it achieves face tracking based on a single camera and realizes cross-camera face tracking based on the spatiotemporal correlation information of a face appearing in the field of view of each camera. Simultaneously, by uniformly processing the identity information of pedestrians on the same trajectory, it improves the accuracy of face recognition.

The utility model provides a power supply circuit of an MCU and a controller, and relates to the technical field of power supply time sequence control. The resistance ratio of the second resistor to the third resistor is equal to the resistance ratio of the fourth resistor to the first resistor, and the potential of the tracking pin of the power supply module is the same as the output voltage of the reference source, so that the potential of the connection point of the second resistor and the third resistor is equal to the potential of the connection point of the fourth resistor and the first resistor; the sum of the partial voltage of the fourth resistor and the partial voltage of the first resistor is equal to the sum of the partial voltage of the second resistor and the partial voltage of the third resistor, so that the output voltage of the first output pin of the power supply module is equal to the output voltage of the second output pin of the power supply module. Therefore, the tracking of the output voltage of the second output pin by the output voltage of the first output pin is realized, so that the output voltage of the first output pin reaches a self set value before the output voltage of the second output pin, and the kernel of the MCU is electrified before the I / O module of the MCU.

The application provides a vehicle lateral control method, system and automobile, comprising the following steps: judging the driving state of the current vehicle according to the current vehicle speed, and selecting a corresponding automatic driving vehicle lateral controller; inputting the current vehicle speed, the vehicle yaw rate, the vehicle mass center side slip angle, the lateral deviation between the vehicle and the target path, the heading angle deviation, and the target path curvature into the corresponding vehicle lateral controller to obtain the total expected steering wheel angle of the vehicle; outputting the final steering wheel angle obtained after limiting the amplitude of the steering wheel angle and the change rate of the steering wheel angle to the steering controller, and then performing steering control according to the final steering wheel angle. The application selects appropriate vehicle lateral controllers for different working conditions, considers the calculation complexity and the control accuracy, and can realize the applicability in all working conditions. Fewer control parameters need to be adjusted, and the working efficiency and the universality can be improved.

This invention belongs to the field of liquid fuel vehicles, specifically relating to a vehicle fuel sampling system. When the controller and the network platform are communicating normally, if the refueling indicator equals the refueling set value, sampling begins sequentially from the sampling chamber according to the sampling indicator, and the corresponding inlet switch is opened. After sampling, the inlet switch of the sampling chamber is closed, and the sampling indicator is stored as the sampling chamber number and the fuel quantity at power-off. The sampling indicator is assigned to the sampling identifier. If the sampling identifier does not equal the set value, the outlet switch of the sampling chamber is opened sequentially to sample the sampling chamber. The designed automatic fuel sampling system is connected to the return fuel line. Combining sampling and sampling control methods, automatic collection, convenient sampling, and fuel tracking of vehicle fuel samples are achieved, providing a key technical solution for the realization of closed-loop fuel quality control.