180results about How to "Implement tracking" patented technology

The invention relates to the field of robot attitude control, in particular to a robot attitude control method and system based on intelligent recognition. The method comprises the following steps: continuously acquiring a real-time sandbox scene video stream based on a high-definition camera; and carrying out time sequence frame segmentation on the real-time sandbox scene video stream, and carrying out global contrast enhancement processing to obtain a global contrast optimization image sequence. Carrying out dynamic object identification on the global contrast optimization image sequence, and carrying out continuous frame optical flow motion tracking to generate a dynamic object optical flow change track; optimizing the image sequence according to the global contrast to extract scene ground and scene obstacle data; performing dynamic trajectory rendering on the scene ground and scene obstacle data based on the dynamic object optical flow change trajectory so as to construct a dynamic scene twinborn model; robot position posture parameters and user operation instructions are recognized. According to the invention, timely attitude adjustment response and accurate movement position control are realized.

The invention discloses a multi-unmanned aerial vehicle target exploration and tracking method and system based on multi-agent ant colony reinforcement learning. The method comprises the following steps: dividing an actual environment for target tracking of unmanned aerial vehicles into grid units; performing target state initialization based on the grid units to obtain an initial observation position of the unmanned aerial vehicle; based on an ant colony algorithm and the initial observation position of the unmanned aerial vehicle, the transition probability of the unmanned aerial vehicle among the grid units is calculated; establishing a reward function based on the transition probability and the grid unit; based on the reward function and the existing exploration information and tracking information, performing centralized training on the multiple unmanned aerial vehicles by using a multi-agent near-end strategy optimization algorithm to obtain an optimal joint decision; and based on the optimal joint decision, enabling the unmanned aerial vehicle to complete exploration and tracking of multiple targets to be explored. According to the technical scheme, the possibility that the unmanned aerial vehicle finds the target in the unknown area is increased, and the target can be effectively followed.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention provides a hovercraft path tracking method and device based on reinforcement learning, equipment and a storage medium, and the method comprises the steps: obtaining the initial positions and motion information of a hovercraft and a dock landing ship, and determining the expected path of the hovercraft based on the initial positions and motion information; calculating a target heading angle of the hovercraft in the expected path based on a first guidance algorithm; taking the target heading angle as input of a reinforcement learning network and an anti-interference controller, generating a first control parameter, training the first control parameter based on a first neural network, and generating a target control parameter; and controlling the hovercraft to move according to the expected path based on the target control parameter.

The invention relates to an automatic image reasoning recognition system and method based on a large model, and the method comprises the steps: obtaining a to-be-processed image, and carrying out the preprocessing of the image, so as to improve the quality of the image; performing global feature extraction on the optimized image through a large-scale pre-training model, and generating a global mask of the image; and according to the global mask of the image, performing automatic identification and labeling on each region in the image, identifying and extracting a target object or a target region in the image through a classification model, performing classification, and according to a classification result, labeling each target object or target region and outputting the labeled target object or target region. According to the method, the identification efficiency and accuracy can be effectively improved, the specific event relationship can be analyzed more reliably, and the development of an intelligent system can be promoted.

The invention discloses a railway inspection scene modeling method based on image recognition. The method comprises the following steps: acquiring a visible light image set and an infrared image set; preprocessing the image; obtaining a first positive example feature map; obtaining a second positive example feature map; fusing the image corresponding to the first positive example feature map under the same semantics and the image corresponding to the second positive example feature map under the same semantics to obtain a fused image; acquiring an image target frame of the fused image; acquiring a corresponding relation between the image target frame and the GPS coordinate position; and constructing a three-dimensional map. According to the invention, the visible light image and the infrared image are fused for use, so that all-time image detection along the railway can be realized; the first positive example feature map and the second positive example feature map are screened, a target frame is positioned for the image with the sensitive event label, and the calculation amount is reduced; the three-dimensional scene is obtained according to the image target frame, and meanwhile, the position of the three-dimensional scene is accurately positioned, so that a worker is assisted to quickly go to an abnormal place, and problem tracking and closed-loop management are realized.

The invention discloses an MPSK demodulation and Doppler tracking combined processing method for an underwater sound direct spread system, and belongs to the field of underwater wireless communication and signal processing thereof. The method specifically comprises the following steps: firstly, receiving underwater direct sequence spread spectrum communication signals at the same time by a multi-channel array element of any array, and preprocessing; then, signal synchronization and Doppler coarse estimation are carried out on each array element; carrying out carrier removal on the received signal of each array element to obtain a baseband signal; then, despreading the passband signal of each channel to obtain a despread related signal, and extracting signal peak information; furthermore, after fine Doppler estimation and compensation are carried out on each channel, multi-channel soft value information combination is carried out. And finally, decoding the merged signal by applying BICM-ID to obtain soft value information of each symbol under high-order modulation, and obtaining information bits carried by each symbol through decoding, the underwater communication and measurement joint integrated technology is realized, and the method has certain anti-noise performance.

The invention discloses a building construction site arrangement optimization method and system and a storage medium. According to the main scheme, the method comprises the steps that topographic data, building layout data and equipment layout data of a building construction site are acquired; drawing a construction site rough arrangement diagram according to the collected building layout data and equipment layout data by using CAD software, and importing the rough arrangement diagram into BIM modeling software to generate a construction site basic model; analyzing the construction site basic model of the BIM technology, and marking a problem area of the basic model; an optimization model is formed by changing the layout of a problem area of the basic model; and analyzing the optimization model to judge whether the optimization model meets the standard or not. According to the technical scheme, intelligent optimization and real-time monitoring of construction site arrangement can be realized, the construction efficiency is improved, the cost is reduced, and the construction safety is enhanced.

A periodic attitude tracking task-oriented unmanned aerial vehicle predetermined time anti-interference learning control method comprises the following steps: establishing a kinetic model of a four-rotor aircraft system, dividing the uncertainty of the system into a periodic uncertainty part and a non-periodic uncertainty part, and initializing the state and control parameters of the system; designing a predetermined time disturbance observer, and quickly estimating and compensating a non-periodic uncertain part; designing a predetermined time sliding mode variable; designing a full-saturation repetitive learning law, and accurately estimating and compensating the uncertain part of the period; and designing a repetitive learning controller based on the predetermined time disturbance observer, the predetermined time sliding mode variable and the full-saturation repetitive learning law. According to the method, the influence of the system uncertainty on the control precision of the unmanned aerial vehicle can be effectively suppressed, and it is ensured that the upper bound of the minimum convergence time of the observation error can be preset by adjusting an adjustable parameter, so that the tracking error is converged to the neighborhood near the original point within the preset time.

The invention discloses a finite time tracking method, system and device for a time-varying heterogeneous formation with an unknown leader system matrix, and a medium, and relates to the field of cluster formation tracking control, and the method comprises the steps: obtaining a communication topological relation of the heterogeneous formation; determining a first follower cluster and a second follower cluster according to the communication topological relation; establishing an Euler-Lagrange kinetic model of the follower and a kinetic model of the leader; respectively establishing distributed finite time observers of the two follower clusters; establishing a distributed finite time time-varying formation tracking controller based on an Euler-Lagrange kinetic model of a follower, a kinetic model of a leader and a distributed finite time observer; and a distributed finite time time-varying formation tracking controller is adopted to control the time-varying formation of followers in the heterogeneous formation. According to the invention, tracking control of the multi-agent formation with an unknown leader system matrix is realized.

The present invention relates to the technical field of ultrasonic diagnosis and treatment. The present invention provides an ultrasonic diagnosis and treatment device and a control method. The ultrasonic diagnosis and treatment device comprises: an ultrasonic transducer device, the ultrasonic transducer device comprises an ultrasonic imaging transducer and an ultrasonic stimulation transducer, the ultrasonic imaging transducer is used to perform ultrasonic imaging of a target area, and the ultrasonic stimulation transducer is used to perform ultrasonic stimulation on peripheral nerves or human epidermis; an ultrasonic electronic system connected to the ultrasonic transducer device, used to control the ultrasonic transducer device and realize ultrasonic imaging or ultrasonic stimulation regulation, the ultrasonic electronic system comprises an ultrasonic imaging circuit module and an ultrasonic stimulation circuit module, wherein the ultrasonic stimulation circuit module comprises a pulse generating circuit, a power amplifying circuit and a matching circuit, and the ultrasonic imaging circuit module comprises an imaging phased array transmitting unit, a receiving unit, an image processing unit and a system control unit; a mechanical arm is connected to the ultrasonic transducer device. Therefore, the present invention can realize the integrated design of peripheral ultrasonic diagnosis and treatment.

The invention discloses a method for counting drill rods of underground drilling rigs based on computer vision, comprising the following steps: S1, obtaining drill rod image data, and dividing the drill rod image data into two types of data; S2, performing image enhancement processing on the two types of data; S3, adding Gaussian white noise of different proportions to each cleaned image; S4, locking a sample image into a 416*416 image after being read in, and each image area passes through Darknet; S5, performing classification and regression tasks on the image candidate frame and the center point, width and height of the candidate frame, and modifying the output setting of the classification task to 1; S6, minimizing the loss function through training, and outputting a plurality of detection candidate frames through the monitoring network; S7, taking the inspection frame with the largest confidence in the inspection frame as the input frame, calculating the IOU of the overlapping inspection frames in turn, filtering when the calculated IOU value is greater than the set threshold, and repeating step S7; S8, tracking and calculating the monitored target by using image classification, Kalman filtering algorithm and Hungarian algorithm.

The invention provides a cross-border logistics trajectory data tracking method and system, and relates to the field of logistics data tracking, and the method comprises the steps: obtaining an original trajectory of a target waybill from a logistics provider query platform corresponding to a waybill number; processing transport nodes in the original track, mapping the transport nodes to preset nodes corresponding to a preset node conversion rule, and determining total node time consumption and total waybill time consumption of the preset nodes; determining a target aging standard corresponding to the logistics product from a preset aging standard library; and processing the total node time consumption of the preset nodes and the total waybill time consumption to obtain a node anomaly judgment result, and traversing all the preset nodes to obtain a cross-border logistics trajectory data tracking result. According to the invention, by integrating the transportation nodes of different logistics providers and the timeliness requirements of different logistics products, unified processing of trajectory data of different logistics providers is realized, more business scenes can be adapted, and the judgment accuracy is improved based on a dual anomaly judgment mechanism of total node time consumption and total waybill time consumption.

The invention relates to the field of anti-unmanned aerial vehicle systems, in particular to a vehicle-mounted anti-unmanned aerial vehicle intelligent photoelectric device and system. The technical problems are as follows: a miniaturized light-weight photoelectric system adopts an FPGA + DSP photoelectric system to track and detect a small unmanned aerial vehicle, the tracking and identification capabilities are weak, the tracking of the unmanned aerial vehicle is unstable, and a target cannot be detected and identified; according to the technical scheme, the vehicle-mounted anti-unmanned aerial vehicle intelligent photoelectric device and system comprise a photoelectric load assembly and a servo control assembly; compared with the prior art that a miniaturized and lightweight photoelectric system adopts an FPGA + DSP photoelectric system to track and detect a small unmanned aerial vehicle, the tracking and recognition capability is weak, the tracking of the unmanned aerial vehicle is unstable, and a target cannot be detected and recognized, the system adopts an intelligent target detection and recognition model based on a YOLOv5 algorithm, and the system has a wide application prospect in a domestic Hisilicon processor Hi3559AV100. 30 frames of images can be processed per second, the detection precision is high, the result is accurate, the anti-interference capability is strong, and the all-weather and all-time target identification requirement can be met.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent crop growth state monitoring method and device based on deep learning, and the method comprises the steps: regularly obtaining pictures of crops in seedling trays in a greenhouse; wherein the seedling tray is divided into a plurality of grids, and is used for performing crop seeding on each grid; on the basis of the trained grid detection model, identifying grids in the crop photo, and counting the number of all the grids; on the basis of the trained crop detection model, identifying leaves of crops in the grids in the crop picture, and marking the contour of each leaf; the crop detection model comprises a seedling detection model and an adult seedling detection model, and calculating various growth state indexes of the crops according to the counted number of all the grids and the identification result of the leaves of the crops in the grids. Therefore, automatic identification and tracking of the growth state of the crop plant are realized, personal errors are reduced, and powerful support is provided for precision agriculture.

The invention, which relates to the technical field of unmanned driving, discloses an unmanned driving control system comprising the following components: an environment sensing layer, a positioning mapping layer, a path planning layer, a decision control layer and a bottom execution layer. According to the invention, the improved YOLOv7-tiny target detection algorithm and the DeepSort target tracking algorithm are adopted, and laser radar point cloud processing is combined, so that accurate identification and tracking of track markers such as cone buckets are realized, the real-time performance and accuracy of target detection are improved, the robustness of environmental perception is further enhanced through a data fusion technology, and the method has the advantages of being high in practicability and easy to popularize. Meanwhile, on the path planning and decision control level, a graph search tree and a B spline curve are used for optimizing the path, and a Stanley / MPC hybrid transverse control algorithm is combined, so that the racing car can still keep stable driving track and speed in high-speed driving and complex racing track environments, and the stability and accuracy of an unmanned driving system are remarkably improved.

The invention provides a flexible trailer detection method and system based on monocular vision. The method comprises the steps that monocular vision information and 3D point cloud information of a trailer are obtained respectively; information fusion is carried out on the monocular vision information and the 3D point cloud information to obtain image information, and the image information at least comprises posture, position and motion state information of the flexible pull part; performing feature extraction and target detection on the image information to obtain target information of the flexible pull part; monocular direction estimation of a front pose is carried out on the target information, and pose information of the flexible pull part is obtained; and performing path planning and decision adjustment based on the pose information in combination with the dynamic change of the front vehicle, and generating a control instruction to control vehicle tracking. According to the invention, the high-efficiency detection of the flexible towed object is realized by using the monocular vision technology, and the safety and the operation efficiency of the towed vehicle are improved.

The invention discloses a traffic target monitoring and tracking system and method based on monocular vision daytime scene reconstruction. The system comprises a monocular camera, an inertial navigation module, a lightweight processor, a radio frequency communication module, a solar energy storage module and a Beidou positioning module. The monocular camera is used for continuously capturing multiple frames of images; the inertial navigation module utilizes a built-in high-precision gyroscope to measure the three-dimensional attitude of the monocular camera during photographing, and calculates an external parameter matrix of the camera; the lightweight processor is responsible for processing multiple frames of images acquired by the camera, performing image preprocessing, vehicle detection and trajectory calculation, and executing a core algorithm; the radio frequency communication module transmits the detected traffic flow information back to the traffic monitoring center; the Beidou positioning module provides geographical location information, so that traffic flow information can be synchronized with the geographical location. On the premise of not depending on high-power-consumption equipment, the vehicle information is transmitted back to the monitoring center through the radio frequency communication module, and the system is suitable for daily traffic monitoring and signal control optimization.

The invention discloses a crane working control method and system based on Beidou positioning and application of the crane working control method and system. According to the scheme, feature parts of a tower crane are positioned in real time through a Beidou positioning module, so that the positions of the feature parts can be updated in time when the crane works, and the working efficiency of the crane is improved. The motion state data of the characteristic parts of the crane can be further obtained by combining with the inertial measurement unit deployed on the crane, and the working state of the crane can be further tracked after the motion state data and the positioning data are fused, so that the working state of the crane can be tracked when a plurality of cranes in a working site serve at the same time; the working interference between the tower cranes can be reduced, and early warning can be provided for background management personnel; in addition, according to the scheme, the possible working range is obtained by simulating the operation instruction, whether the problem of working interference exists or not is judged according to the intersection of action track functions between different cranes, and a mechanism guarantee is provided for safe working of the cranes in a working site.

The invention provides an overvoltage suppression method and device based on adaptive linear active disturbance rejection control, and belongs to the field of power grid control. The method comprises the following steps: acquiring output power and reference power of a target power transmission line; inputting the output power and the reference power into a hybrid power flow controller to obtain a compensation amount of adaptive linear active-disturbance-rejection control (VSC); wherein the VSC comprises a linear expansion state observer, and the linear expansion state observer is used for outputting a state variable estimated value based on the gain parameter and outputting a compensation amount based on the state variable estimated value; the gain parameter is determined according to the bandwidth of the hybrid power flow controller; and suppressing the overvoltage phenomenon of the target power transmission line according to the compensation amount. According to the invention, the problems of hysteresis effect and large ripple in traditional PI adjustment can be solved, the overvoltage phenomenon can be inhibited in time, and the ripple is reduced, so that stable adjustment is realized.

The invention relates to an artificial intelligence information security monitoring method, which belongs to the technical field of artificial intelligence, and comprises the following steps: S1, formulating a security policy: setting a clear security monitoring target and policy, determining a security responsibility subject and responsibility division, and formulating an emergency response plan and a recovery plan; s2, establishing a professional team: establishing an AI security team, establishing a team specially responsible for AI security work, and determining the responsibility of the team; according to the artificial intelligence information security monitoring method, through the AI technology, the content of the monitoring video can be analyzed in real time, the abnormal behavior can be automatically identified, and the alarm can be immediately triggered, so that the response speed and accuracy of the monitoring system are greatly improved, omission and false alarm of manual monitoring are reduced, accurate identification and tracking of the target object can be realized, and the user experience is improved. According to the technical scheme of the invention, the method can achieve the quick locking and continuous tracking of a human face, a vehicle or a specific object, provides powerful support for the subsequent safety investigation, and achieves the high monitoring efficiency and high monitoring accuracy.

The invention discloses an artwork repair management method based on LBS (Location Based Service), and relates to the field of artwork repair management. The equipment with the LBS records events in the process of repairing the artwork by the repairing personnel, forms artwork repairing data with LBS information, uploads the artwork repairing data to the server, and updates the artwork repairing data to a data set generated by each repairing scene of the artwork; before and / or after restoration, special acquisition equipment or equipment with LBS (Location Based Service) acquires the artwork to form artwork feature data, uploads the artwork feature data to a server, and updates the artwork feature data to a data set generated by each restoration scene of the artwork. According to the method, unified process records are formed by using a digital tool, association of space-time and field environment data is established with the process records, an artwork management data basis based on LBS is constructed, multi-dimensional internet-of-things data is archived and comprehensively presented, the authenticity of the space-time and field environment is objectively reflected, and once a problem is found, the problem is solved. And traceability and relief can be carried out through process records.

The invention relates to a GNSS-S radar beam resource optimization method for on-orbit batch ship tracking. The method comprises the following steps: acquiring prior information of ships in a large-range sea surface; establishing a GNSS-S radar batch target tracking system model; the method comprises the following steps: establishing a GNSS-S radar batch target tracking optimization model based on a GNSS-S radar batch target tracking problem; and solving the GNSS-S radar batch target tracking optimization model to obtain a beam pointing distribution and beam residence time distribution scheme, and completing GNSS-S radar beam resource optimization. According to the method, the prior information of the search mode is fully utilized, and the target tracking number is greatly increased through optimal allocation of beam resources.

The invention discloses an electro-hydrostatic actuator simulation method and device based on RBF neural network control, a medium and a product, and relates to electro-hydrostatic actuator emulation.The method comprises the steps that an electro-hydrostatic actuator mathematical model is established, important parameters of a plunger pump and a hydraulic cylinder are obtained, and the parameters of the plunger pump and the hydraulic cylinder are calculated; establishing an electro-hydrostatic actuator simulation model, a signal and interference module and an RBF (Radial Basis Function) neural network control model in the Matlab / Simulink; inputting the position input curves and the interference of the electro-hydrostatic actuator under different working conditions into an RBF neural network control model to realize the rotating speed control of the permanent magnet synchronous motor, thereby obtaining the tracking curves of the electro-hydrostatic actuator under different working conditions; and inputting the tracking curve into an electro-hydrostatic actuator simulation model to realize simulation of the electro-hydrostatic actuator. According to the invention, tracking of the position signal under different working conditions can be realized, influences of disturbance and the like are fully considered, anti-interference performance and tracking precision are improved, control parameters are optimized, and an optimal control effect is achieved.

The invention discloses a multi-target water floating garbage tracking method based on D-StrongSORT (Dempster-StrongSORT). Relates to the technical field of image recognition and tracking. A D-StrongSORT multi-target tracking algorithm combining an appearance model and a motion model is used for tracking floating garbage, a previous detection result video is used as input, a previous position state is associated with a next position state by calculating motion features and appearance features, prediction and tracking of a target position can be well achieved, DIOU is adopted by the D-StrongSORT to replace IOU, and the prediction and tracking efficiency is improved. According to the method, a new feature updating strategy is used to match appearance features of a tracking target to reduce ID switching times of the tracking target, adaptive Kalman filtering is used to reduce error accumulation of a motion model, a new feature updating strategy is used to match the appearance features of the tracking target when an appearance model is established, and better correlation matching of the motion model and the appearance model is realized by designing adaptive weighted cost matrix calculation.

The invention provides a space target tracking method and device using linear array camera push-broom imaging. The method comprises the following steps: calculating a velocity vector and a position vector of a satellite pointing to a space target at a center intersection moment according to position vectors and velocity vectors of the satellite and the space target in an inertial system; obtaining an attitude angle of the satellite pointing to the space target at the center intersection moment based on the position vector of the satellite pointing to the space target; according to the position vector and the relative velocity vector of the satellite pointing to the space target, the attitude angle and the Euler axis of satellite push-broom are obtained; obtaining an inertial quaternion and an inertial angular velocity of the space target at the current moment based on an attitude angle of the satellite pointing to the space target, an attitude angle of satellite push-broom and an Euler axis; and according to the inertial quaternion and the inertial angular velocity of the space target, calculating the attitude control moment of the satellite at the current moment so as to drive the satellite to track the space target in real time. According to the scheme, high-resolution imaging of the space target can be realized.

The invention discloses a visual navigation and direction control system for an autonomous vehicle, and the system comprises a Beidou positioning receiver which is used for receiving a Beidou satellite signal and calculating the position and speed of the vehicle; the camera array comprises a front-view camera, a side-view camera and a rear-view camera and is used for capturing images of the surrounding environment of the vehicle; the laser radar is used for measuring distances and contours of surrounding objects; and the radar system is used for detecting long-distance objects and speed. The high-precision positioning service of the Beidou satellite navigation system is utilized, the centimeter-level positioning precision is provided, and the stable positioning performance can be kept even in a complex environment; an advanced sensor fusion technology is adopted, data of a camera, a laser radar, a radar and an ultrasonic sensor are integrated, and the accuracy and robustness of data processing are improved; in combination with a visual navigation and processing technology, accurate detection, identification, tracking and semantic segmentation of a target in a complex traffic scene are realized, and adaptability and accuracy of visual processing are improved.

The invention discloses a pasture sheep flock detection and tracking method based on an unmanned aerial vehicle and a safety early warning system, and belongs to the technical field of animal husbandry automatic management, and the method comprises the following steps: S1, data collection and preprocessing; s2, constructing and training a detection model; and S3, sheep flock tracking. Through the aerial work capacity of the unmanned aerial vehicle platform, the field of view of monitoring is greatly expanded, the labor cost is reduced, the timeliness and accuracy of monitoring are improved, and animal husbandry managers are helped to master the dynamic state of a sheep flock in time and make scientific decisions; through a proposed target detection model and a multi-target tracking algorithm, high recognition rate and tracking stability can still be kept in a complex environment, the problems of target shielding and the like are effectively solved, and accurate detection and tracking of sheep flock targets are realized; through the safety early warning system, missing livestock conditions can be found and reported in time, and powerful emergency response support is provided for animal husbandry managers.

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 invention relates to the technical field of wheel monitoring, in particular to a wheel polygon monitoring method based on an improved self-adaptive wave trap, which can accurately and efficiently represent and quantify wheel polygon characteristics in real time and realize wheel state monitoring. The method is applied to the state monitoring and detection application of the polygon of the high-speed train wheel with harmonic characteristics for the first time. By introducing an adjustable factor into a traditional NG algorithm, compared with a traditional ANF, the improved MANF has a higher convergence speed and still keeps good robustness in a large-noise environment, wheel polygon features can be accurately and efficiently represented and quantified in real time, and wheel state monitoring is achieved.