199 results about "Object motion" patented technology

The invention provides an object motion trail prediction method and device, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: reconstructing an obtained multi-view time sequence two-dimensional image sequence of a target object, obtaining an original particle point cloud sequence, and generating a sparse key point cloud sequence after sampling; constructing a particle map sequence based on the spatial position of the key point at each moment in the sequence, and performing spatial semantic completion; performing object-level dynamic space-time aggregation on the complemented key point feature sequence to obtain aggregated key point features, inputting the aggregated key point features into a particle diagram converter, and outputting key point updating features after long-range force propagation modeling through the converter; and decoding and predicting key point displacement at a future moment based on the updated feature, and generating a future movement track of the target object according to the key point displacement. Based on the method, the invention further provides a device for predicting the object motion trail. According to the method, the fine appearance and the stable and accurate motion trail of the object can be predicted at the same time.

The invention discloses an object level motion blur three-dimensional scene synthesis method and an object level motion blur three-dimensional scene synthesis device based on three-dimensional Gaussian splash (3D Gaussian splash, 3DGS). According to the method, a multi-view image and corresponding camera parameters are used as input, and three-dimensional Gaussian point scene representation including spatial position, scale, rotation, opacity and appearance characteristics is constructed; generating a binary mask of a moving foreground object and a static background by using an image segmentation model; on the basis, camera exposure time is dispersed into a plurality of time steps, space pose parameters changing along with time are introduced only for three-dimensional Gaussian points corresponding to the foreground object, instantaneous three-dimensional scene representation under each time step is obtained, integration or weighted averaging is carried out on rendering results of the plurality of time steps, and the real-time three-dimensional scene representation of the foreground object is obtained. Generating a motion blur rendering result with real physical significance; meanwhile, a mask-guided foreground-background separation rendering strategy and a partition loss function are adopted, clear rendering supervision is applied to a static background area, fuzzy rendering supervision is applied to a moving foreground area, and joint optimization of three-dimensional Gaussian scene parameters is achieved; in the reasoning stage, the clear background and the fuzzy foreground are fused and output according to the mask, and a three-dimensional scene reconstruction result with the clear background and the reasonable motion fuzzy effect of the foreground is obtained. According to the method, the exposure integral process and the object movement track are explicitly modeled in the three-dimensional geometric space, and object-level controllable fuzzy three-dimensional scene generation is realized.

An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. An artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.

To provide a method for generating a dataset of robot motion programs for training a path generation neural network.SOLUTION: A large language model uses a robot task library and an asset geometry database as inputs. The large language model breaks down a task into steps, and then generates code describing a robot and object motion to complete the task. The generated code produces robot motions for the task, and a corresponding robot motion program is created and executed in simulation. The simulated robot motion programs are used to generate collision-free robot paths via RRT and / or optimization, and the collision-free paths are validated for robot reachability and object placement success. The validated motion programs are added to a dataset and used for training a path generation neural network.SELECTED DRAWING: Figure 3

This disclosure relates to a virtual object motion generation method and apparatus, and a computer device. The method includes: parsing the motion description text to obtain respective motion description information of the plurality of semantic levels; separately encoding the motion description information of the plurality of semantic levels to obtain respective motion description representations of the plurality of semantic levels; performing denoising processing at the first semantic level on the sampled noise signal based on a motion description representation of the first semantic level, to obtain a motion eigenvector; performing, at each semantic level after the first semantic level, denoising processing on the sampled noise signal based on a motion eigenvector and respective motion description representations of at least two semantic levels from the first semantic level to the current semantic level, to obtain a motion eigenvector; and decoding the motion eigenvector to obtain the virtual object motion.

The present invention discloses a robot dynamic grasping method based on a sliding window self-attention mechanism. The method first installs a camera and calculates the transformation matrix between the camera and the manipulator coordinate system based on the hand-eye calibration technology; uses affine transformation to process the static data set to generate a grasping dynamic data set with speed information; then constructs a dynamic grasping network model based on the sliding window multi-head self-attention mechanism, and designs a loss function optimization model that simultaneously considers the object's motion speed and grasping posture; finally, deploys the trained model into a robot grasping environment, designs a position-based visual servo grasping control scheme, and realizes the follow-up movement and grasping action of the manipulator end effector to the grasped object. The robot dynamic grasping method of the present invention can quickly and accurately predict the grasping posture and motion speed of an object based on a set of continuous single-channel depth images collected by the camera, solving the grasping problem of general objects in complex dynamic environments.

The invention provides a progressive motion-power alignment smart operation rapid migration method and system. Relates to the technical field of robot operation control. The method comprises the following steps: extracting hand-object movement and interaction information from an RGB hand-object interaction video; based on kinematics matching, mapping the hand operation track into a primary control signal of the target dexterous hand; performing dynamic optimization on the primary control signal in combination with a multi-level reward mechanism under a reinforcement learning framework, and generating a stable grabbing control strategy; a dexterous wrist control signal is synthesized according to the object target trajectory, and the semantic consistency of operation tasks is kept; and through a multi-dexterous-hand configuration adaptation mechanism, a control signal is migrated to dexterous hands of different configurations. According to the method, the human hand operation track can be automatically converted into control instructions of multiple types of dexterous hands, so that the dexterous hands have the capability of executing complex tasks such as grabbing, moving and operation, the training data acquisition cost of the dexterous hands is reduced, and the cross-equipment operation reusability and generalization capability are improved.

The invention provides a spatial intelligent visual physical process inference method based on an implicit physical large model, and belongs to the field of spatial intelligent and artificial intelligence modeling calculation. The problems of low prediction precision and lack of physical consistency for complex physical scenes in the prior art are solved. The method comprises the following specific steps: acquiring multi-modal data of an environment, and representing the multi-modal data in a unified coordinate system; preprocessing the multi-modal data, designing a geometric coding model, a dynamic prediction model and an energy conservation constraint, introducing a space-time attention mechanism, predicting the motion state of an object according to the multi-modal data, and obtaining prediction data of the motion state of the object; acquiring real observation data of object motion, comparing the difference between the prediction data and the real observation data, and performing correction and weight updating on the prediction data of the model through a self-adaptive residual term; according to the method, visual information and implicit physical law modeling are fused, and the self-supervised physical consistency constraint is utilized, so that automatic learning and prediction of a potential physical process are realized.

Provided are a method and device for driving movement of a target object by a gravitational field generated by a varying electromagnetic field which relate to the technical field of physics. The method includes: generating, by a variable electromagnetic field generator, a time-varying electromagnetic field in space where a target is located; adjusting a frequency, amplitude and phase of the time-varying electromagnetic field so as to generate an effect of a varying magnetic field in the space; and based on the time-varying electromagnetic field, generating an acting force on the target object to drive the target object to move along a predetermined trajectory or direction. The present invention breaks through limitation that the traditional electromagnetic mechanics can only drive metal materials, utilizes the varying characteristics of the electromagnetic field to drive and realize accurate control of movement of all objects comprising a non-magnetic substance and a non-conductive substance.

An object motion pose reproduction method based on a Marker ball comprises the following steps that firstly, size measurement is carried out on a target object, a model file is generated through three-dimensional scanning, and denoising and zooming optimization are carried out; then, a reflective Marker ball is pasted on the surface of an object, and three-dimensional coordinate data of the reflective Marker ball are collected through an optical motion capture system; then, a template group is constructed based on the initial Marker ball position of the object, a target group is constructed in combination with motion capture data, and a rigid body transformation matrix containing a rotation matrix and a displacement vector is solved through least square optimization; and finally, acting the transformation matrix on the model file to reproduce the pose, and reversely optimizing the modeling precision or the Marker point position through error analysis. The object motion pose reproduction method is high in precision, strong in anti-interference capability, simple in algorithm and suitable for the fields of industrial automation, movie and television games and the like.

The invention discloses a positioning and mapping method, system and device based on dynamic vision and a medium, and relates to the technical field of simultaneous positioning and mapping, and the method comprises the steps: carrying out the feature point extraction based on the dynamic detection information of a dynamic object, and obtaining a target feature set; compensating the motion track of the dynamic object based on the visual sensor prediction pose, and obtaining the motion state of the object; determining an object detection optimization variable based on the object motion state, the target feature set and the visual sensor motion parameters; performing real-time detection on the dynamic object by fusing the object detection optimization variable, obtaining a real-time detection result of the dynamic object and generating a static map to which the object belongs; according to the method, the motion trail of the dynamic object can be accurately tracked in real time, so that the positioning precision and the real-time performance of the dynamic object are improved, and the dynamic obstacle detection efficiency and the static map construction precision are further improved.

The invention discloses a convergence judgment method for a round or spherical part layout system, electronic equipment and a storage medium. The method comprises the following steps: dispersing each circle or ball in a container into independent units by adopting a discrete unit method; selecting a model according to the collision deformation degree; the contact force between the circles or the balls is calculated based on the impulse principle; establishing a motion differential equation of a single circle or ball in the vertical direction, and obtaining a second-order transfer function; respectively establishing a time domain model and a complex frequency domain model, and performing simulation verification on convergence under single disturbance and multi-disturbance; by adjusting the disturbance quantity value, the action time and the included angle between the circle center connecting line of the two intersecting circles and the horizontal direction, the steps are repeated to predict convergence under different conditions. According to the method, efficient and accurate convergence judgment can be provided for the field of layout optimization for different types of round (ball) objects, the utilization rate of raw materials is maximized, and waste materials are reduced. And the problem of layout divergence caused by unstable object movement is avoided.

Certain aspects of the present disclosure provide techniques and apparatus for accurately predicting the movement and location of objects in a multidimensional space. An example method generally includes receiving an input defining a location of an object in a three-dimensional space at a first time. The input generally includes a plurality of tokens. Using a transformer neural network, a predicted movement for tokens in the plurality of tokens is generated. Generally, the predicted movement includes a translation and a rotation associated with individual tokens in the plurality of tokens. The predicted movement is translated into an object-wise predicted movement. Based on the object-wise predicted movement and the location of the object in the three-dimensional space, a predicted location of the object at a second time subsequent to the first time is output.

Estimating motion of a human or other object in video is a common computer task with applications in robotics, sports, mixed reality, etc. However, motion estimation becomes difficult when the camera capturing the video is moving, because the observed object and camera motions are entangled. The present disclosure provides for joint estimation of the motion of a camera and the motion of articulated objects captured in video by the camera.

The invention discloses a real-time three-dimensional modeling and hanging object size identification method and system applied to a tower crane, and belongs to the technical field of image processing, and the method comprises the following steps: obtaining a corrected real-time three-dimensional model, thereby obtaining a hanging object simulation dynamic swing amplitude; the movement envelope volume of the hanging object is obtained; whether static obstacle interference exists or not is judged, if yes, early warning is carried out, and if not, hoisting task construction is carried out; when hoisting task construction is carried out, whether dynamic obstacle interference exists or not is predicted in real time, if yes, corresponding speed adjustment is carried out, the obstacle avoidance speed is obtained for initial configuration, and if not, speed adjustment is not executed; according to the method, the mechanical performance influence parameters of the tower crane are obtained, the mechanical performance influence value is obtained through analysis, the obstacle avoidance speed is corrected, the correction execution obstacle avoidance speed is obtained, and actual configuration is carried out, so that the problem of inaccurate obstacle avoidance speed adjustment caused by inaccurate suspended object size analysis in the prior art is solved; the method is suitable for a tower crane construction scene and a port container lifting remote control scene.

The embodiment of the invention provides a man-machine interaction method and device, equipment, a medium and a program product. The method comprises the steps that target media data are displayed in a target interface, and the target media data are currently played media data; in response to a starting operation of a target service, object motion information is determined according to device attitude data sent by an attitude data acquisition device, the target service comprises interaction configuration information, and the interaction configuration information represents candidate object motion information and a candidate interaction mode; and determining a target interaction mode according to the object motion information and the interaction configuration information, and executing an interaction operation on the target media data according to the target interaction mode. According to the embodiment of the invention, man-machine interaction is controlled based on different motion information of the object, the user is prevented from being in the same posture for a long time, and the user is guided to do proper motion and healthily and properly use the application program.

The application provides a kind of progressive motion-power alignment dexterous operation fast migration method and system. It is related to the technical field of robot operation control, the method of the application comprises: extracting hand-object motion and interaction information from RGB hand-object interaction video;Based on kinematic matching, the human hand operation trajectory is mapped to the primary control signal of the target dexterous hand;Under the reinforcement learning framework, the primary control signal is dynamically optimized in combination with a multi-level reward mechanism to generate a stable grasping control strategy;According to the object target trajectory, the dexterous hand wrist control signal is synthesized to maintain the consistency of the operation task semantics;Through multi-dexterous hand configuration adaptation mechanism, the control signal is migrated to different configurations of dexterous hand.The application can automatically convert human hand operation trajectory into control instructions of multiple types of dexterous hand, enabling it to perform complex tasks such as grasping, moving and operating, thereby reducing the cost of acquiring training data for dexterous hand and improving the operation reusability and generalization ability across devices.

A method and system for evaluation of object motion repetitiveness as a basis to distinguish between human motion and non-human motion, in order to facilitate control of device operation. An example method includes (i) a computing system receiving sensor data representing object motion detected by at least one sensor, the object motion defining motion of an object, (ii) the computing system making a determination, based at least on an evaluation of the received sensor data, of whether the detected object motion is repetitive, and (iii) based at least on the determination being that the detected object motion is not repetitive, the computing system responding to the detected object motion by causing a device to take an action corresponding with a prediction that the object is a human being.

The application relates to the technical field of computer vision, and discloses a target object motion state analysis method, device and equipment, the method comprising the following steps: reconstructing based on multiple images shot by a camera during target object motion to obtain multiple position points; determining an initial motion interval of the target object based on a preset starting point and a preset landing point; correcting the initial motion interval to obtain a target motion interval; performing trajectory fitting based on the target motion interval to obtain a flight trajectory; and performing prediction based on the flight trajectory to obtain a predicted landing point. The application preliminarily determines the initial motion interval by extracting the position information of the target object, thereby obtaining an accurate target motion interval; the flight trajectory is obtained by performing trajectory fitting based on the target motion interval, which is helpful for intuitively understanding the motion state of the target object; the landing point of the target object is predicted based on the trajectory, and the accuracy of trajectory fitting and landing point prediction is improved, so that accurate analysis of the motion state of the target object is realized.

A method for generating a dataset of robot motion programs for training a path generation neural network. A large language model is used to configure a task environment and generate code which creates robot simulations. The large language model uses a robot task library and an asset geometry database as inputs. Based on the task and asset inputs and a task instruction, the large language model breaks down the task into steps, then generates code describing robot and object motion to complete the task. The generated code produces robot motions for the task, and a corresponding robot motion program is created and executed in simulation. The simulated robot motion programs are used to generate collision-free robot paths via RRT and / or optimization, and collision-free paths are validated for robot reachability and object placement success. Validated motion programs are added to the dataset and used for training the path generation neural network.

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.

A control apparatus that obtains a correction amount for correcting a motion blur attributed to a motion of an object is disclosed. The apparatus computes a first motion blur amount based on a position of an object region in a captured image and a second motion blur amount based on an object vector indicating a motion of an object between captured images. The apparatus obtains a motion blur correction amount by adding the first motion blur amount and the second motion blur amount with use of weights corresponding to relative reliabilities of the first motion blur amount and the second motion blur amount.

A video game system and method analyze virtual contact between an avatar and a virtual object within a video game. The point of contact of the virtual contact on the virtual object and / or the intensity of contact of the virtual contact may then be used to determine a subsequent virtual action to be performed within the video game. The virtual action, with any virtual movement thereof, may be carried out in a realistic manner within the video game by determining a virtual trajectory of the motion. The virtual trajectory may be determined using a motion model. The motion model may provide the virtual trajectory of the virtual object based at least in part on one or more parameters of the virtual object, such as a weight parameter. The motion model may be trained using training video clips with realistic motion of virtual objects.

A method for generating a dataset of robot motion programs to train a neural network for trajectory generation. A large language model is used to configure a task environment and generate code that creates robot simulations. The large language model takes a robot task library and an asset geometry database as inputs. Based on the task and asset inputs and a task statement, the large language model breaks the task down into steps and then generates code that describes the robot and object motion to perform the task. The generated code creates robot motions for the task, and a corresponding robot motion program is created and executed in a simulation.The simulated robot motion programs are used to generate collision-free robot trajectories using RRT and / or optimization. These collision-free trajectories are validated for robot reachability and object placement success. Validated motion programs are added to the dataset and used to train the neural network for trajectory generation.

This invention provides a three-dimensional measurement method for the surface of a moving object on a production line based on phase-shifting profilometry. Employing a three-dimensional vision measurement system composed of a projector and a camera, and based on the measurement principle of phase-shifting profilometry, the method analyzes the phase error caused by the motion of the measured object and proposes a three-dimensional measurement method for the surface of a moving object that does not require phase error correction. By setting the installation position and angle of the projector and camera, the phase encoding direction in the projected stripe pattern is perpendicular to the direction of motion of the measured object, thereby eliminating the phase error caused by motion. This allows the three-dimensional surface measurement results of the measured object under motion conditions to essentially achieve the measurement accuracy of the measured object under static conditions, realizing high-precision online real-time measurement of the surface three-dimensional morphology of moving objects on actual production lines.

1. The name of the design product: electromagnetic punch card time recorder. 2. The use of the design product: used as a kind of experimental instrument for studying the acceleration of object motion in physical experiment. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective view 1.

The invention relates to the technical field of inertial sensors, in particular to an online calibration method, device and system for a gyroscope, and the method comprises the steps: obtaining the triaxial angular velocity of the gyroscope when the gyroscope measures the movement of an object, and the vibration data of the gyroscope in each axial direction when the gyroscope is fixed on the object in real time; obtaining a resonance deviation estimation value in each axial direction at each moment, and obtaining a similarity factor in each axial direction at each moment by combining the similarity between the angular velocity in each axial direction and the vibration data; acquiring a distribution coefficient in each axial direction at each moment through vibration data distribution in each axial direction before each moment in the current running process of the gyroscope, and acquiring a calibration coefficient in each axial direction at each moment in combination with the similarity factor; obtaining an aging coefficient of the gyroscope at each moment; and obtaining the attitude angle in each axial direction at each moment. The invention aims to improve the calibration precision of the gyroscope.

The application discloses a reference video object segmentation method based on semantic consistency and motion perception. The method comprises the following steps: 1. constructing semantic prompt information and video frame information of the reference video object segmentation dataset; 2. preprocessing the reference video object segmentation dataset; 3. establishing a reference video object segmentation model based on semantic consistency and motion perception: designing a double-branch decoupling strategy to decouple feature information at the semantic and visual levels, thereby extracting static and motion information of text description and visual features; designing a hierarchical motion perception module to capture and align motion information between different frames, analyze short-term and long-term motion information, thereby enabling the model to obtain the perception ability of long-term motion patterns; designing a semantic consistency module to align semantic description and video features, thereby improving the correctness of target selection and the integrity of the mask, and avoiding false detection of negative samples; designing a perception dynamic fusion mechanism to embed text information into the visual feature space, enabling the visual features to obtain text semantic information, thereby enhancing the cross-modal understanding ability of the model; 4. constructing a loss function, updating the model parameters, setting the training parameters, training, and obtaining the best weight; 5. detecting the test set image based on the best weight to obtain the final segmentation result. The application effectively decouples static and dynamic information, enhances the perception ability of object motion patterns, and improves the segmentation performance.

A method, a device (800), a medium for controlling the movement of a virtual body (107), including: a target determining step, in the first frame, based on the moving direction and position of the virtual body (107), selecting a virtual object (103) in a predetermined area as a virtual target (202); a dynamic adjusting step, calculating a horizontal moving direction (302a) of the virtual body (107), a direction of a connecting line between a vertical projection (107′a) of the virtual body (107) on the horizontal plane and a vertical projection of the virtual target on the horizontal plane, and an included angle (θ1) between the horizontal moving direction (302a) of the virtual body (107) and the direction of the connecting line in each frame, and controlling the horizontal moving direction (302a) of the virtual body (107) based on the included angle (θ1) (204).