1054 results about "Motion parameter" patented technology

The invention relates to the technical field of camera motion compensation, and discloses an IMU-based camera motion compensation method and device and a storage medium, and the method comprises the steps: processing original data collected by a plurality of IMU sensors, and obtaining six-degree-of-freedom IMU fusion data; processing through a linear convolution aliasing model to obtain a high-dimensional motion feature vector; performing fusion to obtain a camera motion parameter prediction value; constructing an advanced network compensation model, carrying out jitter intensity evaluation, and dynamically adjusting a compensation model parameter matrix; performing geometric transformation to obtain an initial stable image sequence and a foreground mask; according to the method, IMU data and image information can be effectively fused, camera motion parameters can be accurately estimated, and a compensation strategy can be dynamically adjusted according to scene features to realize camera motion compensation.

The present application relates to the field of robot control. Disclosed are a control method and apparatus for a legged robot, and a legged robot and a medium. The method comprises: acquiring body sensing information and external sensing information, wherein the body sensing information is used for representing the motion state of the body of a legged robot, and the external sensing information is used for representing environmental information around the legged robot; inputting the body sensing information and the external sensing information into a deep neural network, so as to obtain a first prediction residual that is output by the deep neural network; adjusting a trajectory generation parameter of a foot trajectory generator on the basis of the first prediction residual; and on the basis of a first joint motion parameter that is output by the parameter-adjusted foot trajectory generator, controlling the motion state of the legged robot. By using the solution provided in the embodiments of the present application, the flexibility and stability of a legged robot when moving in a complex environment are improved.

The invention provides a real-time code spraying and position retesting method and system for a positioning and testing robot in signal construction, and relates to the technical field of robot positioning and testing, and the method comprises the steps: obtaining the three-dimensional point cloud data of a construction area, and generating a grid map; identifying a positioning mark point and planning an optimal motion track; controlling the fixed measurement robot to move and keeping the code spraying device horizontal; when a preset position is reached, the height of the nozzle is adjusted and code spraying is performed; collecting a code spraying image and extracting contour features; comparing the actual sprayed code with a preset template, and calculating a deviation; optimizing the motion parameter when the deviation exceeds a threshold; and uploading the deviation data and generating a quality report. The method improves the accuracy and automation degree of signal construction.

The invention relates to the technical field of model construction, in particular to a method and a system for constructing a sheet metal working three-dimensional process model. The method comprises the following steps: acquiring equipment specification data and material characteristic data; the method comprises the following steps: acquiring geometric parameters and motion parameters of sheet metal processing equipment by using a three-dimensional scanner, and integrating equipment specification data and material characteristic data to construct a sheet metal three-dimensional initial process model; analyzing a material plastic deformation characteristic of the material characteristic data and setting a processing parameter threshold range; the machining parameter threshold range is input into the three-dimensional initial process model for stage machining process simulation, the flange area strain rate, the dangerous section thinning rate and the rebound angle variable quantity are output, and stage machining process simulation comprises plate inflow stage simulation, forming stage simulation and shaping stage simulation. According to the method, three-dimensional scanning, comprehensive machining process simulation, topological optimization and a real-time error feedback mechanism are introduced, so that the construction accuracy of the sheet metal machining process model is improved.

The invention relates to the technical field of robots, and discloses a robot nondestructive testing method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-modal sensing data and motion state parameters; constructing a three-dimensional material feature model and analyzing to generate an initial defect feature evaluation model; loading material attributes and detection precision constraints to generate a comprehensive model, and combining motion parameter simulation to obtain path regulation and control data; training the dynamic defect prediction model to obtain a detection path correction model and generating initial detection parameters; obtaining dynamic correction simulation information based on the initial parameters and the like; constructing a multi-objective optimization model, and optimizing to obtain an optimal detection path parameter; and generating a safety detection probability by combining real-time model reasoning, and adjusting a detection strategy to realize dynamic path matching. The system comprises a data acquisition module, a feature modeling and analysis module, a comprehensive evaluation and simulation module and the like. The method improves the detection precision, efficiency and adaptability, and is suitable for nondestructive detection of complex targets.

The invention provides a numerical control machine tool dynamic precision control method and system based on multi-axis linkage error compensation, and relates to the technical field of mechanical manufacturing, and the method comprises the steps: collecting shaft system motion parameters and real-time cutting load parameters of a multi-axis numerical control machine tool in a linkage machining process in real time; shafting motion parameters comprise displacement, speed and acceleration of each feed shaft, and cutting load parameters are obtained through inversion of main shaft current and servo driving torque; calculating a dynamic following error of each feed shaft in a linkage state based on the shaft system motion parameters; and carrying out load coupling correction on the dynamic following error by fusing real-time cutting load parameters to obtain an initial multi-axis linkage dynamic error. According to the invention, real-time precision control in the high-dynamic machining process can be realized.

Provided in the embodiments of the present application are a robot positioning method and a robot. The robot positioning method comprises: acquiring motion parameters corresponding to a current frame of point cloud which is collected by a robot and motion parameters corresponding to a previous frame of point cloud which is collected by the robot, wherein the motion parameters at least comprise a linear velocity and an angular velocity; at least performing angle filtering, line filtering and voxel filtering processing on the current frame of point cloud, so as to obtain a current frame of optimized point cloud, wherein the angle filtering is used for removing noise points from the current frame of point cloud, the line filtering is used for extracting a structured scene from the current frame of point cloud, and the voxel filtering is used for clustering feature points in the current frame of point cloud; and on the basis of motion parameters corresponding the current frame of optimized point cloud and the motion parameters corresponding to the previous frame of point cloud, calculating a pose corresponding to the robot when same collects the current frame of point cloud, so as to realize positioning. The method can reduce the computation amount of a robot when performing positioning in a complex scenario, thus reducing the computing power requirements for the robot.

The invention relates to a dynamic precision compensation control method based on an intelligent PCB drilling machine, and the method comprises the steps: collecting a dynamic parameter set in real time through a multi-source sensor group disposed on a drilling main shaft, the dynamic parameter set comprises displacement deviation caused by axial deformation of a main shaft, thermal deformation analyzed based on interlayer temperature gradient and frequency spectrum characteristics reflecting tool-material coupling vibration, and according to the multi-dimensional compensation vector and current drilling process parameters, a space trajectory correction instruction and a motion parameter optimization instruction are dynamically synthesized, so that the dynamic parameter set is obtained. Wherein the trajectory correction instruction reconstructs a real-time interpolation path through a B-spline curve, the motion controller receives a space trajectory correction instruction and a motion parameter optimization instruction, at least three times of compensation iteration are completed in a single drilling period, a linear motor and a spindle motor are driven to execute online precision compensation, and the execution delay of the compensation instruction is smaller than 2 ms.

The invention relates to the technical field of two-dimensional motion control, and discloses a high-precision two-dimensional motion error prediction compensation iteration method. The method comprises the following steps: on an operation interface of a motion control system, generating a motion track overview containing a target position sequence and corresponding expected motion parameters according to parameters set by a user, and connecting the motion track overview with an actual motion execution device; measuring the deviation between the actual position and the target position of the motion platform at the current sampling moment, and calculating a motion error vector; constructing a prediction model based on the motion error vector and the expected motion parameter, and predicting a two-dimensional motion error at a future moment through iterative optimization; generating a compensation control instruction according to the prediction result and applying the compensation control instruction to the motion execution device; and monitoring the compensated motion state in real time, updating the prediction model and outputting a compensation log. According to the method, by predicting the error in advance and iteratively optimizing compensation, the two-dimensional motion control precision can be effectively improved, the real-time performance and adaptability of compensation are enhanced, and the method is suitable for a high-precision motion control scene.

The invention provides a distributed robot cooperative control method and system based on edge computing. Environmental space data and dynamic obstacle motion parameters of robots are collected, and cross-robot joint compression is carried out to obtain a distributed sensing data set. Then, based on the spatial density difference of the data set, hierarchical communication links in the group and the priorities of signal transmission paths of the hierarchical communication links are dynamically constructed and adjusted in real time. Then, the adjusted priority is converted into a communication weight parameter, a cooperative control instruction set is generated in combination with an obstacle motion parameter, and cross-node synchronization is carried out on the cooperative control instruction set and obstacle information sensed by each node; and finally, the synchronized instruction set is utilized to drive the robot to execute edge computing resource interaction, and a cooperative motion path is generated. According to the technical scheme provided by the invention, the communication efficiency and the adaptive capability of distributed robot cooperative control can be improved.

The invention discloses a motion error dynamic compensation method applied to a surgical mechanical arm, and belongs to the technical field of surgical mechanical arm control and medical automation. The method comprises the following steps: firstly, acquiring position data of each joint encoder of the surgical mechanical arm, a motor current signal, a position feedback signal of an end effector, load change information and accumulative operation duration in real time; processing the data, fusing the data through adaptive Kalman filtering to estimate a joint dynamic error, inputting a load change rate and an accumulated operation time length into a pre-training error prediction model to output a compensation basic quantity, and fusing the load change rate and the accumulated operation time length to generate a real-time error compensation quantity; and then the compensation amount is superposed to a joint original control instruction to correct motion parameters, and finally the compensated tail end position deviation is analyzed, and a precision identifier or an adjustment prompt is generated and fed back to a control system. According to the method, real-time error compensation is achieved, the compensation precision and working condition adaptability are improved, it is guaranteed that the mechanical arm is stably positioned, and the surgical operation risk is reduced.

The invention relates to a ball action evaluation method, device and equipment based on a large model, and a medium, and the method comprises the steps: collecting the three-dimensional coordinates of the joints of a student and the trajectory data of sports equipment, and generating a standardized action sequence through space-time compensation and noise suppression; based on inverse kinematics and differential kinematics technologies, human body joint rotation features and instrument motion parameters are separated and extracted, and a multi-dimensional motion feature vector is constructed; calculating a dynamic mahalanobis distance and a key joint contribution degree by adopting a comparative learning mechanism and a standard action template, and generating a quantitative evaluation result; and finally, through inverse kinematics optimization and three-dimensional visual rendering, outputting an action correction scheme. According to the method, the limitation of traditional single-mode evaluation is broken through, deep analysis of instrument-human body collaborative motion features is realized, the technical problems of inaccurate action detail capture, single evaluation dimension and insufficient feedback visualization in a high-speed scene are effectively solved, and the scientificity and real-time performance of training guidance are remarkably improved.

The invention relates to the technical field of image and video recognition, in particular to a trajectory tracking method and system based on laser radar and camera fusion perception. The method comprises the following steps: monitoring motion parameters of a target object; when the motion parameter of the target object reaches a preset monitoring threshold value, judging that the target object enters a monitoring state, and recording initial motion planning information; the three-dimensional motion direction information of the target object is obtained through the laser radar, and the two-dimensional dynamic image information of the target object is obtained through the camera. Through a data processing technology, an image processing technology and a video identification technology, three-dimensional motion direction information and a two-dimensional dynamic image of a target object are obtained, space-time correlation is carried out, and target fusion track information is constructed; and the trajectory deviation of the target fusion trajectory information is timely and accurately identified, and dynamic trajectory correction is performed on the trajectory deviation, so that the trajectory tracking accuracy is improved.

In an embodiment, a method for human motion generation with open vocabulary scene-and-text context is provided. The method involves receiving an input that includes a 3D point cloud of a scene containing a goal object with a natural language instruction related to the goal object. A text tokenizer is applied to the text to obtain tokenized text, and a text encoder from a pre-trained vision-language model generates text features. First scene features are generated by applying a pre-trained U-Net scene encoder to the 3D point cloud, which are down sampled to obtain second scene features. A conditional latent is obtained by fusing the second scene features with the text features. A conditional motion generator predicts motion parameters for a parametric human body model over a specific time duration. Finally, 3D human meshes for multiple motion frames are obtained based on the motion parameters and the parametric human body model.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention relates to a human body posture capture-based disabled assisting robot planning method and system, and the method comprises the following steps: S1, a patient is fixed to a system main body through a flexible fixing device, so that the posture capture precision is ensured, and accidental sliding is prevented; s2, a high-definition RGB camera and a depth sensor are used, real-time posture joint point data of the patient are obtained based on a human body posture estimation algorithm, and the real-time posture joint point data comprise the head position, the limb position and the trunk position; s3, resolving the captured data in real time, and referring to a posture database to generate a most suitable initial rehabilitation posture; s4, collecting visual, voice and physiological signals by using a camera, a microphone and other sensors, and performing feature extraction and fusion by using a convolutional neural network to improve the recognition accuracy; s5, judging the satisfaction degree of the patient to the current posture through an emotion recognition technology in combination with voice emotion analysis, and adjusting the system according to the satisfaction degree of the patient; the motion parameters of rehabilitation training of the patient are obtained quickly, the effectiveness and rehabilitation efficiency of rehabilitation training are improved, and the participation degree and enthusiasm of the patient are increased.

The invention relates to the technical field of obstacle recognition, in particular to an obstacle state recognition method and device in an automatic driving environment of a sightseeing park, and the method comprises the steps: setting corresponding motion models for different target obstacles; environment data and motion parameters are collected in real time through a multi-sensor module carried on a vehicle; performing preprocessing and feature extraction on the environment data to identify the type of a target obstacle; obtaining a prediction path of each obstacle based on the motion model and the motion parameters; and outputting the predicted path to a control system of the vehicle to perform an obstacle avoidance operation. The objective of the invention is to greatly improve the recognition efficiency of vehicles in a sightseeing park and reduce the recognition failure rate while guaranteeing the experience of tourists.

The invention discloses a sports evaluation method and system based on computer vision, and belongs to the field of sports intelligent evaluation, and the method comprises the following steps: S1, collecting a motion image; s2, matching and fusing the moving images under multiple view angles; s3, processing the motion video stream; s4, data preprocessing; s5, motion tracks of the athlete and the target are obtained, and motion parameters of key parts of the athlete are extracted; s6, comparing the motion parameters of the key part of the athlete with preset data, outputting an evaluation result according to a comparison result, and meanwhile, evaluating whether the speed change and the route of the athlete are compliant or not by analyzing a motion track; and S7, encrypting the evaluation result, and uploading the encrypted evaluation result to a user interaction interface for display. By the adoption of the sports evaluation method and system based on computer vision, evaluation accuracy can be improved, meanwhile, more visual and effective training suggestions are provided for athletes, and therefore the competitive level is promoted to be improved.

The present application belongs to the technical field of image processing. Disclosed are a visual tracking method and apparatus for a target object, and a storage medium and a device. The method comprises: extracting an image frame from a video stream captured by a gimbal camera; tracking a target object in the image frame, so as to obtain a tracking result, wherein the tracking result is used for reflecting the position of the target object in the image frame; generating an attitude parameter of the gimbal camera according to the tracking result, and adjusting the attitude of the gimbal camera according to the attitude parameter, such that a moving target object is always located at a predetermined position in a viewfinder frame of the gimbal camera, wherein the attitude parameter comprises a yaw angle parameter; and generating a motion parameter of a chassis according to the attitude parameter and the tracking result, and according to the motion parameter, controlling the chassis to move along with the target object. The present application can achieve the purpose of controlling a chassis to move along with a target object, so as to ensure that the target object is always located in the angle-of-view range of a gimbal camera, thereby continuously tracking the target object.

Embodiments disclosed herein relate to electronic devices, and more particularly to estimating a damage risk level for an electronic device. A method disclosed herein includes measuring motion parameters of the electronic device, on the electronic device being dropped on a surface. The method further includes classifying the surface into at least one type by processing the measured motion parameters using a classifier module. The method further includes estimating a damage risk score depicting the damage risk level for the electronic device based on the classified surface, the measured motion parameters of the electronic device, and a usage history of the electronic device.

The invention discloses a human motion optimization method and system for a multi-lens video, a terminal and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: performing shot switching point detection and multi-level segmentation on a multi-shot video frame sequence to obtain a target video frame sequence; calculating a target camera track and a human body motion parameter according to the target video frame sequence; and obtaining a target human body motion parameter of the multi-shot video frame sequence based on the target camera track and the human body motion parameter. The method comprises the following steps: firstly, segmenting a multi-shot video frame sequence into a plurality of target video frame sequences without shot switching points according to the shot switching points; and calculating a target camera track and a human body motion parameter according to each target video frame sequence. And finally, the human body motion state in the video is analyzed by combining the target camera track and the human body motion parameters, so that target human body motion parameters with high precision, continuity and physical consistency are obtained. The method is suitable for various application scenes such as motion capture, virtual reality and sports analysis.

The invention provides a cleaning robot multi-mode motion control system, method and equipment and a medium, and belongs to the technical field of intelligent robot control. The system comprises a multi-modal data acquisition module used for performing multi-source synchronous acquisition on an environment image, distance information, a voice signal and a collision signal and outputting original sensing data; the data fusion and processing module is used for receiving the original sensing data, sequentially executing space-time alignment correction, multi-source feature fusion and instruction semantic analysis, and outputting an environment sensing result and a structured control instruction; the motion planning and decision-making module is used for executing global path planning, dynamic obstacle trajectory prediction and motion parameter decision-making based on the environment perception result and the structured control instruction, and outputting a motion control instruction containing a motion mode, a speed and an angle; and the motion control execution module is used for receiving the motion control instruction, converting the motion control instruction into an execution mechanism driving signal, realizing closed-loop control through real-time state feedback and outputting a motion execution result.

The invention belongs to the technical field of robot control, and provides a robot self-adaptive task planning and action adjusting method and system, and the method comprises the steps: responding to a task instruction, and calibrating the motion parameters of a mechanical arm of a robot; acquiring a state vector according to a set frequency, determining whether visual abnormality or motion abnormality exists or not according to a continuous change condition of the state vector, and generating an abnormality mark if the visual abnormality or motion abnormality exists; performing action correction in response to the abnormal mark or the task failure mark, performing cross-modal fusion on the current state vector and the semantic vector of the task instruction, generating a candidate action sequence based on the fused feature and task target, and considering the compatibility of the candidate action and the current environment, the residual step number of the action queue and the task failure mark. An optimal action is screened out, and then an action adjusting instruction is generated; and continuously obtaining the state vector and the action correction process until the robot completes the task instruction. The environment adaptability and the execution success rate of the robot in a complex scene can be improved.

The present disclosure belongs to the technical field of robot control, and relates to a method and apparatus for controlling a robot, and a medium and an electronic device. The method comprises: acquiring a target picture, which is collected by a camera apparatus that is mounted on a robot, and acquiring a target instruction for controlling the robot; inputting the target picture into a trained visual model, such that the trained visual model outputs a visual embedding vector; acquiring a text embedding vector corresponding to the target instruction, and inputting the visual embedding vector and the text embedding vector into a trained language model, such that the trained language model outputs a target numerical value; and decoding the target numerical value, so as to obtain a movement parameter, wherein the movement parameter is used for controlling the movement of the robot. By means of the present disclosure, a robot is driven to perform various motions to complete an instruction of a user, such that human-robot interaction is more intelligent and freer, thereby meeting usage requirements in various application scenarios, increasing the success rate of specific task execution, and enlarging a usage range of the robot.

The invention discloses a railway comprehensive defect detection method and system based on multi-source sensor fusion. The method comprises the following steps: carrying out joint calibration on static external parameters by adopting a multi-modal calibration plate; a high-precision timestamp and vehicle multi-source motion parameters are utilized to construct a motion compensation model to carry out adaptive dynamic distortion correction calibration; synchronously acquiring multi-source sensor data, and based on static external parameters and adaptive dynamic distortion correction, obtaining a corrected visible light image, an infrared image and a laser radar three-dimensional point cloud; complementarity features are extracted from the data of the three modes, adaptive fusion and optimization are carried out through an attention mechanism, and multi-scale fusion features are output; segmenting different railway facility areas by using multi-scale fusion features, extracting corresponding area features, and calling detection algorithms of corresponding facilities to identify defects of different railway facilities; according to the method, high-precision calibration, feature-level fusion and railway facility special defect detection are fused, and the automation level and the detection accuracy of railway inspection are improved.

The invention belongs to the technical field of multi-camera cooperative imaging, and discloses a multi-camera cooperative anti-shake control system and method. The method comprises the following steps: generating a global hardware trigger signal by using an FPGA (Field Programmable Gate Array), synchronizing IMU and multi-camera acquisition, and combining motion parameters to obtain a space-time aligned multi-view image; obtaining a global jitter component through feature extraction, motion vector field generation, depth map analysis and the like; fusing the data with IMU data to generate a trajectory, and inputting the trajectory into LSTM to obtain future compensation amount; and cutting based on deep layered transformation to obtain an EIS optimized frame, dynamically electing a main camera for super-resolution reconstruction, and outputting a final anti-shake video stream. Through multi-source information fusion, layered precise compensation and dynamic optimization, the accuracy and stability of motion estimation are remarkably improved, the jitter influence of the finally output video stream is effectively weakened, and the reliability and visual coherence of the image stabilization effect in a complex scene are ensured.

The invention discloses a video gait analysis-based asthenia syndrome evaluation method, medium and equipment, and the method comprises the steps: firstly, synchronously collecting gait videos of different visual angles through a plurality of cameras, and extracting 2D joint point sequences of all visual angles through a stacked hourglass network; then, a high-precision 3D joint point time sequence is reconstructed through a multi-view fusion algorithm, depth features are extracted through a residual connection network, and data of all view angles are dynamically weighted and fused based on the shielding rate and the confidence coefficient; performing sliding window segmentation and downsampling on the 3D sequence, and performing action classification by using an LSTM time sequence model; and finally, key motion parameters are extracted from a classification result to calculate a weakness score, and weakness grade evaluation is realized. According to the method, the problem of single-view shielding is solved through multi-view 3D reconstruction, the evaluation precision is remarkably improved by combining dynamic weighted fusion and time sequence modeling, and compared with a traditional method, the method has the advantages of being non-contact, low in cost and high in objectivity, and is suitable for large-scale screening of community and family environments.

An AGV robot vision acquisition and avoidance control method based on deep thinking relates to the field of non-electrical variable control or adjustment systems, and comprises the following steps: acquiring real-time motion parameters of an automatic guided vehicle, calculating a key path point set and constructing feature vectors; collecting a visual image and depth data, extracting obstacle features through grid division, and generating a three-dimensional feature model; mapping the path points to a three-dimensional model to generate a dynamic scene sequence, and calculating a distance value with a dynamic target to determine a risk level; analyzing the scene by using a large language model, generating an obstacle avoidance scheme containing an obstacle avoidance track, speed and steering, and storing the obstacle avoidance scheme in a scheme library; current scene features are obtained, candidate schemes are selected through similarity matching, and the optimal scheme is selected for execution through safety scoring. By implementing the method, the obstacle avoidance success rate of the automated guided vehicle can be improved.

The invention discloses a rotor motion planning method in a conveying system and the conveying system, the conveying system comprises a plurality of rotors, the method comprises the following steps: determining the number and the motion trail of the plurality of rotors, designing the motion parameters of the plurality of rotors, and designing the motion parameters of the plurality of rotors to be the same, the motion parameters comprise at least one of an acceleration change rule, a speed change rule and a rotor position change rule of the rotor. Therefore, the plurality of rotors can move according to the uniformly set motion parameters, so that the movement of the plurality of rotors is uniform, the buffer time of the anti-collision design of the rotors in an asynchronous control state is saved, and the working efficiency is greatly improved.

The invention provides a circuit board component accurate positioning and mounting method and system based on visual guidance, and relates to the technical field of mounting, and the method comprises the steps: obtaining the multi-angle image information of a to-be-mounted component, determining a three-dimensional point cloud model and a depth map sequence, and determining the type information; segmenting the point cloud model through a region growing algorithm to extract features for pose estimation; the mounting parameters are called and combined with the posture deviation information to control a mechanical arm; and motion parameters are optimized based on a self-adaptive optical flow algorithm until the precision requirement is met. According to the invention, the component mounting precision and efficiency are improved, and the mounting failure rate is reduced.