316 results about "Motion parameter" patented technology

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.

A method for motion synchronization-based head pose estimation, by a head mounted display (HMD) device and an HMD device for performing the same are provided. The method includes receiving, by the HMD device, motion data from a plurality of motion sensors of the HMD device, receiving, by the HMD device, a plurality of image frames from at least one simultaneous localization and mapping SLAM camera of the HMD device, estimating, by the HMD device, a plurality of motion parameters of head movements of a user from the plurality of image frames received from memory, generating, by the HMD device, a filtered subset of the motion data received from the plurality of motion sensors based on the plurality of motion parameters of the head movements, synchronizing, by the HMD device, the plurality of image frames received from the memory and filtered subset of motion data, and estimating, by the HMD device, the head pose based on the synchronized plurality of image frames and motion data.

The invention discloses a neural signal-based wounded rehabilitation effect evaluation model construction method, and relates to the technical field of medical rehabilitation, and the method comprises the steps: synchronously collecting electroencephalogram, myoelectricity and motion parameters, extracting multi-source features to construct feature vectors corresponding to training actions, and according to the feature vector data of multiple training actions of a wounded, constructing a neural signal-based wounded rehabilitation effect evaluation model; and capturing dynamic changes of neural signals and action control ability in the training action sequence based on the sequence model, and constructing an evaluation model. According to the invention, time-dependent learning is carried out on the corresponding characteristics of each training action by using the sequence model, dynamic changes of neural signals and action control ability along with the training actions can be captured, an improvement trend in a rehabilitation process is embodied, and an evaluation model aiming at an action training effect is constructed; the action indexes output by the evaluation model can reflect the instant effect and gradual improvement condition of each action in training, and help to identify rehabilitation inflection points and training bottlenecks.

The invention relates to a self-adaptive movement method and system based on an intelligent rehabilitation device, and relates to the field of intelligent rehabilitation devices.The method comprises the steps that biomechanical data are collected; extracting multi-dimensional motion features from the biomechanical data, and combining a preset multi-stage standard motion template library to obtain a current motion state; identifying a current motion phase based on the current motion state; matching a standard motion template of a corresponding stage from a multi-stage standard motion template library according to the current motion stage; combining the current motion state with a standard motion template to obtain a motion parameter deviation risk value; when the motion parameter deviation risk value exceeds a preset risk threshold value, generating a motion adjustment strategy according to the standard motion template and the motion parameter deviation risk value; and performing adaptive motion adjustment on the intelligent rehabilitation device based on the motion adjustment strategy. The method has the effect of adapting to the actual motion states of the user in different stages.

The invention discloses a steel plate cutting method and device, a medium and equipment, and belongs to the field of cutting. According to the method, three-dimensional coordinates of all points on the surface of the head of a steel plate are generated by obtaining real-time motion parameters of the steel plate and the head distance and angle detected by a millimeter-wave radar and combining a preset coordinate conversion model. And then, converting the three-dimensional coordinates into a steel plate head shape image by using a morphological reconstruction model constructed based on preset three-dimensional point cloud data and morphological feature extraction rules. According to the steel plate cutting method and device, accurate positioning and efficient cutting are achieved, the production efficiency and the material utilization rate are improved, and the problem that the steel plate cannot be accurately cut in the prior art is effectively solved.

The invention provides a teleoperation method and a robot. The method comprises the following steps: receiving operator attitude data sent by an inertial measurement unit IMU system; according to the operator attitude data, target attitude data of the humanoid robot are determined, and the target attitude data comprise the tail end target pose and the target arm angle of the seven-degree-of-freedom mechanical arm; the arm angle is a dihedral angle between a plane where the shoulder part, the elbow part and the wrist part of the seven-degree-of-freedom mechanical arm are located and a target pose and a target arm angle surface of a reference flat tail end; according to the target posture data, motion parameters of all joints of the humanoid robot are determined; and generating a control instruction according to the motion parameters. By means of the method, the uncertainty of null-space motion can be effectively eliminated, and the robot motion is more personified.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a projectile motion three-dimensional reconstruction method and device based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting and preprocessing a simulation data set containing an RGB image sequence and an asynchronous event stream, so as to generate an event representation diagram; jointly segmenting the event representation graph to obtain a background and a projectile target; establishing a 3D normalized coordinate system taking the projectile target as the center to obtain an initial geometric reference; based on a 3D Gaussian splashing method, generating a Gaussian kernel rendering image according to the initial geometric reference, and performing multi-modal joint loss optimization and Gaussian kernel screening on the Gaussian kernel rendering image to obtain a static Gaussian field; and mapping the static Gaussian field to a real coordinate system, and fusing multiple modes, gravity constraint and optical flow smooth loss to optimize the camera pose so as to generate projectile motion parameters. Therefore, the problems that in a traditional reconstruction method, the edge of a high-speed target is fuzzy, optimization is difficult due to no initial geometric reference, and the motion trail lacks physical consistency are solved.

The invention discloses a star point motion parameter estimation method for a high-dynamic star sensor. The method specifically comprises the following steps: (1) acquiring a star map under a dynamic condition through a fixed star sensor; (2) carrying out filtering preprocessing on the fixed star sensor image obtained in the step (1); (3) carrying out threshold segmentation on the image processed in the step (2) and carrying out connected domain extraction to obtain rough star points; (4) selecting the brightest star point in the star points obtained in the step (3); (5) obtaining a sub-pixel star point track by using the brightest star point adjacent area obtained in the step (4) and adopting a Zernike moment sub-pixel edge extraction method; and (6) utilizing the sub-pixel star point trajectory obtained in the step (5), and adopting least square fitting to obtain star point motion parameters. The method has the beneficial effects that the precision of star point trailing length and angle estimation is improved, so that the extraction precision of star points in a dynamic environment is improved, and the precision of a fixed star sensor is improved.

The invention discloses a museum audience behavior analysis system integrating positioning and digital twinning. The system comprises an audience real-time positioning module, an exhibition hall collection digital twinning module, an audience behavior analysis module and a behavior feedback adaptation module. The audience real-time positioning module adopts a multi-source positioning data fusion algorithm to obtain audience three-dimensional position coordinates and motion parameters; the exhibition hall collection digital twinning module reconstructs an exhibition hall collection three-dimensional model based on multi-modal data, and an integrated positioning data interface maps the positions and tracks of audiences in the twinning model in real time; the audience behavior analysis module extracts audience behavior features, establishes association between the features and exhibit positions and exhibition area attributes in the twin model through a behavior-scene association algorithm, and outputs a behavior analysis result; and the behavior feedback adaptation module dynamically adjusts the display strategy according to the analysis result. According to the system, deep fusion of positioning and digital twinning is realized, the accuracy of audience behavior analysis is improved, and support is provided for exhibition hall operation optimization and visiting experience upgrading.

The application discloses a digital twin mechanical arm control method based on a motion sensor, and comprises the following steps: step one, building a digital twin model of a mechanical arm; step two, establishing wireless communication between the motion sensor and a computer; step three, using a Kalman filter to solve the posture, and solving the motion data of the motion sensor into current posture information; after obtaining the posture information, the angle and displacement of each joint of the mechanical arm at the target posture are calculated through a mechanical arm inverse kinematics algorithm; step four, sending the motion parameters to the controller of the mechanical arm through TCP communication to control the motion of the mechanical arm to the specified position, and unity sends the motion of each life cycle to the digital twin model, so that the digital twin mechanical arm follows the motion of the real mechanical arm. The application solves the life and health problems of workers when they control the mechanical arm at a close distance in a harsh working environment.

The invention discloses a gyro accelerometer dynamic error modeling and calibration method considering angular motion of a base, and belongs to the field of dynamics and control. The implementation method comprises the following steps: establishing kinematics and dynamics equations of the PIGA under the triaxial angular motion of the base, and deducing a complete error model comprising a static error term, a dynamic error term and a nonlinear error term; constructing a kinetic equation including inertia tensor, a moment balance equation and servo loop influence; quantitatively analyzing the influence of the angular motion parameters of the base on PIGA output errors; based on three-axis turntable experimental design, dynamic response of each axis of the PIGA is excited through single-axis, double-axis and three-axis angular velocity input, and error model coefficients are separated and calibrated; error model parameters are calibrated by adopting a mode of combining a least square method and other data fitting algorithms; angular velocity and angular acceleration terms are separated based on direction cosine matrix transformation, and error coefficient aliasing is avoided; dynamic response data are fitted through a least square method, and all error coefficients are calibrated at a time.

The invention provides a robust path planning method and system for a picking robot, and the method comprises the steps: S1, setting a reward function based on robot information; digitally modeling an actual operation scene as a training set, and carrying out reinforcement learning and training on an original robot agent to obtain a trained agent; s2, constructing an experience library for the difficult scene; s3, enabling the trained intelligent agent to explore a target scene, and collecting environment characteristics of the target scene; corresponding experience in an experience library is matched, and a motion path of the trained intelligent agent is planned according to information of the experience; s4, constructing a strategy reconstruction function, and calculating and obtaining motion parameters of movement of an end effector of the trained agent; and S5, enabling the robot serving as the trained intelligent agent to complete the task. According to the invention, when the picking robot faces a complex scene, real-time strategy fusion is carried out through retrieval of the experience library and the output of the original intelligent agent so as to improve the real-time performance and accuracy of the path planning algorithm.

This invention belongs to the field of image processing technology and discloses a method, system, and terminal for continuous image target detection considering relative target motion. It utilizes an image target detector to detect targets in an image at time k, obtaining the corresponding detection result Y. k ; Regarding the detector's detection result Y k Y k Temporal matching is performed with the detection results obtained by the algorithm at the previous time step; based on the average brightness index g of the image at time k. k Estimate the reliability parameter p of the test results k Calculate the noise matrix R(k) and the predicted bounding box size b″ of the detection results at time k, respectively. k The Kalman filter is applied to the position of the object at time k, and the bounding box size of the object at time k is estimated by weighted fusion to form the result of image detection at time k. This invention can estimate the motion parameters and size variation patterns of targets in images based on target detection results in continuous images, providing effective data support for subsequent target localization and actual motion state estimation.

Video coding using warped motion compensation is described. Extended rotations for the warped motion compensation can be explicitly signaled. For example, motion parameters for predicting the current block and a rotation angle can be decoded. A warping matrix is obtained using the motion parameters and the rotation angle, and a prediction block is obtained by projecting the current block to a quadrilateral in a reference frame. Also described is determining a prediction model of the current block and obtaining a prediction block by projecting the current block to a quadrilateral in a reference frame. Determining the prediction model can include determining whether to predict the current block using a motion vector, a local warping model, or a global motion model, obtaining motion parameters of the prediction model, decoding a rotation angle, and obtaining a warping matrix using the motion parameters and the rotation angle.

The invention discloses a small target detection method based on multi-agent reinforcement learning, and the method comprises the steps: inputting a to-be-detected image into an agent motion parameter model, and outputting to obtain a motion parameter and a decision vector; a first intelligent agent in the intelligent agent execution model is used for image feature extraction and fusion of global features and local features of the image, and a second intelligent agent is used for multiple image enhancement processing of the image; the agent execution model processes the to-be-detected image according to the action parameters and the decision vector; and inputting an output result of the agent execution model into the target detection model, and outputting to obtain a target recognition result. According to the technical scheme, the to-be-detected image is detected in advance through the intelligent agent action parameter model, the image preprocessing mode more suitable for the to-be-detected image is determined, the corresponding image preprocessing mode is executed through the intelligent agent execution model, and the small target detection accuracy is effectively improved.

This application relates to a method and system for loading 3D models using oblique photogrammetry based on dynamic trajectory prediction. The method includes the following steps: real-time acquisition of camera motion parameters and identification of camera motion modes; acquisition of moving region information and generation of a first preloaded tile set using a fan-shaped region prediction algorithm; generation of predicted spatiotemporal position information using a motion prediction model and collision detection with the scene octree index to generate a second preloaded tile set; acquisition of current load information and weight allocation to generate a preload list; identification of the priority order in the preload list and loading the 3D model target tiles into video memory using a multi-level cache scheduling mechanism. In summary, this application, by combining dynamic trajectory prediction with a multi-level cache scheduling mechanism, achieves intelligent response and efficient data loading for complex camera motion modes, significantly improving data loading efficiency and rendering smoothness in complex motion scenes.

The application relates to the technical field of photoetching machine workbench, and discloses a photoetching machine high-speed high-precision workbench positioning calibration method.The method comprises the following steps: collecting workbench position data and motion parameters, analyzing position error and motion stability matching degree, and generating a position error control parameter set; then, based on the parameter set, extracting environmental interference values and gradient change amounts, analyzing the influence of environmental interference on positioning accuracy, adjusting environmental compensation parameters to generate an environmental optimization parameter set; subsequently, according to the environmental optimization parameter set, analyzing dynamic response under high-speed motion of the workbench, adjusting control input rate and feedback path distribution proportion, redistributing control parameter distribution trend and dynamic value, and obtaining a dynamic adjustment result; finally, based on the dynamic adjustment result, generating an abnormal intervention data set, and then analyzing target position distribution proportion and calibration time, adjusting target calibration path parameters, and generating a positioning calibration data table.

A method for controlling motion parameters of a pepper harvester based on combination of point clouds and images, including: (1) performing space-time calibration on a radar and a camera, constructing a data collection platform, and pre-processing image and point cloud data; (2) acquiring a real-time number of pepper in front of a harvester and an average height from canopies of pepper plants to soil through a point cloud, acquiring a cutting width and a pepper fruit proportion in front of the harvester through an image, and acquiring a comprehensive lowermost location of pepper fruits by combination of the point cloud and the image; (3) acquiring real-time motion parameters of the pepper harvester; and (4) controlling a real-time rotation speed, operation speed, and height of a drum using a fuzzy PID control method based on the comprehensive lowermost location of the pepper fruits and a predicted value of a feeding quantity.

The invention discloses a self-adaptive hybrid control grinding method and system for a steel rail welding seam. The method comprises the steps that a welding seam three-dimensional model is obtained through 3D vision, and material removal amount distribution is calculated; intelligent layered path planning is conducted based on the distribution, specifically, process threshold layering is set, section sampling is conducted to generate tool points, collision-free optimization is conducted, an initial polishing path is generated, and expected contact force is matched; in the polishing process, the contact force is collected in real time through a six-dimensional force sensor; feedforward control is carried out on the material removal amount based on visual prediction, feedback compensation is carried out through a fuzzy PID controller on the basis of force deviation fed back by a force sensor, motion parameters of the robot are dynamically adjusted, and double-layer self-adaptive control of visual intelligent planning and force control accurate execution is achieved. The system comprises corresponding modules. The problems that an existing method is poor in adaptability to geometric differences of the welding seams and lacks macroscopic planning are solved, and high-precision, high-efficiency and high-consistency automatic grinding of the steel rail welding seams is achieved.

The invention relates to the technical field of mobile robot navigation, and discloses a robot adaptive motion control method and system, and the system comprises a step test module which is used for recording odometer data and control quantity data with a timestamp; the parameter fitting module is used for analyzing the odometer data and the control quantity data and establishing a mapping function of the control quantity and the movement speed; the delay compensation module is used for maintaining a historical control quantity queue and predicting the expected pose of the robot when the control instruction sent at the moment t is executed based on the control delay tau; the multi-objective optimization module is used for solving the optimal control quantity; and the online updating module is used for continuously collecting motion data in the motion process of the robot and dynamically updating the mapping function and the key parameters. According to the method, actual motion parameters are automatically obtained through step testing, different robot individuals and performance attenuation scenes are adapted, the problem of model mismatching is solved, and time delay compensation is controlled to reduce positioning deviation.

This disclosure provides systems, methods, and devices that utilize machine learning models to determine corresponding spatial positions and motion trajectories for images. In one aspect, a method is provided that includes receiving an image of a scene captured by a camera; determining, with a first machine learning model, a plurality of position values relative to the camera for at least a subset of pixels within the image; and training a second machine learning model based on the determined position values. The method further includes determining basis trajectories based on movement of the positions relative to previous image frames, and determining, for each of a subset of pixels, a movement trajectory relative to the previous frames as a weighted combination of the basis trajectories. These techniques can be employed as part of a structure-from-motion pipeline to estimate multi-view three-dimensional geometry, camera poses, and per-pixel object motion. Additional aspects are provided.

This invention discloses a self-reconfiguration method for free-form modular robots. First, an abstract model of the basic elements of a class of free-form modular robots is constructed, where each robot module can be connected to each other from any direction via active and passive connectors. Then, based on a hardware prototype, a reasonable action space and observation space are designed for this type of resource-constrained modular robot. The action space includes the drive parameters of two planar continuous motion motors and the motion parameters of one connector, while the observation space includes the motion information and local connectivity relationships of the robot itself and its neighboring modules. The reconfiguration problem of the free-form modular robot is modeled as a distributed partially observable Markov decision process. Based on a proximal policy optimization algorithm, an efficient and conflict-free multi-agent reinforcement learning method for reconfiguration in the continuous action space can be achieved. Finally, the timely termination of the reconfiguration process is achieved under the conditions of local observability and limited communication.

This invention discloses an adaptive cruise control primary target selection method and apparatus, comprising: estimating the vehicle's trajectory based on its yaw rate; correcting the motion parameters of each target vehicle within a target range according to the trajectory; calculating the motion index of the target vehicle based on the corrected motion parameters; determining the driving state of the target vehicle by comparing the motion index with a corresponding index threshold; determining a representative driving state for the current scenario based on the driving states of all target vehicles; and determining the target vehicle with the closest relative distance to the vehicle among those that meet the corresponding driving state as the primary target, and outputting the primary target distance and primary target speed. This invention incorporates multiple variable factors, refines the calculation corrections under different scenarios, and improves the control accuracy and safety of the adaptive cruise control system.

The invention provides a motion data processing method and mobile equipment, and relates to the technical field of terminals. In the movement process of the user, the real-time position of the user is obtained within the first duration, for example, the positioning signal within the first duration is relatively strong, and the stride of the user is obtained according to the real-time position. And in a second duration, for example, when a positioning signal is relatively weak, acquiring motion parameters such as a distance, a set speed, an average speed and the like according to the stride of the user and the corresponding step number. Even if the positioning signal is poor and the real-time position of the user cannot be determined, the motion parameters can be obtained and displayed to the user, and the problem that the motion parameters are lost when the positioning signal is poor can be avoided.

A control system for a material processing application includes a sensor device and a controller. The sensor device is configured to receive actual motion data and actual process data from an apparatus executing a material processing application. The controller includes logic to determine an adjusted motion parameter and an adjusted process parameter based on the actual motion data and the actual process data, and execute the adjusted motion parameter and the adjusted process parameter to achieve a performance goal.

The invention provides a control method and system for preventing rear-end collision of a bus and a medium. The method comprises the steps of collecting structure parameters and motion parameters of a preset bus, synchronously obtaining state parameters of a target object, performing processing according to the structure parameters and the motion parameters, obtaining a vehicle body envelope safety margin, judging whether the target object belongs to a driving path target object or not, and if the target object belongs to the driving path target object, judging whether the target object belongs to the driving path target object. The method comprises the following steps: acquiring lane characteristic parameters of a lane where a preset bus is located, processing to obtain a lane occupancy rate, acquiring response state parameters of a driver of the preset bus, processing to obtain a driver response time estimation, extracting a time collision threshold value, performing weighting processing in combination with the lane occupancy rate and the driver response time estimation, and obtaining a rear-end collision risk coefficient. And a rear-end collision risk level is determined and a corresponding braking intervention strategy is adopted, so that a rear-end collision prevention control technology of the bus is realized.

The invention relates to the technical field of laser equipment repair, in particular to a laser cladding repair method which comprises the following steps: A, constructing a data analysis space of an x-z section of a rolling cutter to be repaired and a rotating angle omega of a positioner; b, a laser scanner is used for obtaining analysis space data of the rolling tool to be repaired; c, importing the analysis space data into a computer, and comparing the analysis space data with original analysis space data of the rolling cutter to be repaired to obtain a wear difference data model; d, calling a cladding powder process by the computer, and automatically calculating motion parameters of the robot and the positioner by utilizing a preset formula to obtain a cladding path; and E, the robot cooperates with the positioner operation program to conduct laser cladding repair. The surface abrasion of the rolling cutter to be repaired can be efficiently repaired.

Multiple motion parameters are determined from the simulated object mesh. These motion parameters include information on the virtual character's motion velocity, the object's deformation rate, and the object's deformation region size. Based on the first audio parameter control and the multiple motion parameters, a friction sound is retrieved from a friction audio database containing multiple sample friction sounds. The first audio parameter control is configured to control the characteristics of the friction sound. Based on the second audio parameter control and the multiple motion parameters, a crumpling sound is retrieved from a crumpling audio database containing multiple sample crumpling sounds. The second audio parameter control is configured to control the characteristics of the crumpling sound.

There is provided a method and system for monitoring the health of a solenoid and controlling the solenoid to compensate for a change in the health of the solenoid due to wear. The system including a sensor for detecting at least one parameter of a motion of the solenoid; and a control system for providing a control signal to the solenoid at a first time point, measuring at least one motion parameter of an actual motion of the solenoid driven by the provision of the control signal, and predicting a remaining life of the solenoid based on a difference between actual and target motion parameters of the motion of the solenoid.

This invention discloses a method and system for martial arts movement instruction and digital dissemination based on digital twins. It acquires three-dimensional joint coordinates and motion parameter data from the practitioner's limbs using motion sensors. A preset coordinate transformation algorithm processes the data to form a standardized input sequence, resulting in standardized limb joint data. This constructs a digital twin model of the practitioner. Simultaneously, a standard template model of the corresponding martial arts movement is retrieved from a preset database. A three-dimensional mapping algorithm is used to overlay and compare the two models to determine the initial joint position matching degree. If the matching degree is lower than a preset threshold, a deviation detection mechanism is triggered. Joint angle deviation and movement rhythm error indicators are extracted from the comparison results, and quantified values ​​are calculated using machine learning algorithms to obtain the deviation analysis results. This invention improves the accuracy and safety of training, significantly enhances the overall instruction effect, and provides an innovative technical path for the digital presentation, immersive dissemination, and cultural inheritance of traditional martial arts.