53 results about "Motion dynamics" patented technology

A joint simulation and operation operability evaluation method based on wind power operation and maintenance ship motion dynamic response and active compensation gangway ladder motion control comprises the steps that an operation and maintenance ship frequency domain seakeeping model is built, and a hydrodynamic coefficient library is calculated; wave exciting force is reconstructed based on a Cummins time domain motion equation, and ship motion response prediction is realized by adopting implicit generalized alpha time domain composite integral; the dynamic positioning system extracts low-frequency motion through a Kalman filter and optimizes thruster thrust distribution to stabilize the position of the ship. A gangway ladder dynamic model is established based on a Lagrange equation, real-time control over displacement, speed and acceleration of the top end of the gangway ladder is achieved through a feedforward-feedback composite control strategy, and nonlinear output of a hydraulic actuator is optimized. And realizing joint simulation of the coupling system through time domain synchronization and self-adaptive PID (Proportion Integration Differentiation) control. And checking gangway ladder movement based on a preset threshold value and a space boundary condition, and evaluating the operability of the climbing operation. According to the method, the limitation of traditional single-disciplinary analysis is broken through, and the forecasting precision of wind power operation and maintenance operation operability is effectively improved.

The invention belongs to the field of track planning and intelligent control of a reusable vehicle, and relates to an intelligent track planning method for a cruise section of the reusable vehicle. The method comprises the following steps: constructing a three-degree-of-freedom centroid motion dynamics model of a cruise section vehicle, generating an offline optimal trajectory sample library covering wide working conditions based on a Gaussian pseudo-spectral method, designing a mapping relation between deep neural network learning task parameters and trajectory features, and outputting a high-quality trajectory planning initial value online. The distribution of collocation points is dynamically adjusted in combination with a self-adaptive collocation point method, a nonlinear programming problem is iteratively solved with a high-quality initial value as a starting point, and efficient and accurate optimization of the trajectory is achieved. Simulation results show that the method significantly reduces the sensitivity of online optimization to initial guess, still has fast response ability and high robustness under complex constraint and uncertainty working conditions, effectively improves the autonomous trajectory planning ability of the cruise section of the reusable vehicle, and ensures flight reliability and control precision.

The invention discloses an action error prevention method and system based on AI identification. The method comprises the following steps: acquiring a standard action video and a to-be-detected action video; inputting the standard action video into a dynamic serialization analysis model for training, and extracting movement track features of standard human skeleton points and standard key objects; inputting the to-be-detected action video into the trained dynamic serialization analysis model to extract motion track features of to-be-detected human skeleton points and to-be-detected key objects; and comparing the motion trail characteristics of the standard motion with the motion trail characteristics of the to-be-detected motion to obtain an error value, and comparing the error value with a preset threshold to judge whether the motion is wrong or not. The method can overcome the defects that a traditional detection method is low in efficiency, difficult to capture motion dynamic characteristics, poor in adaptability and the like, manual operation errors can be found in time, and the product quality and the production efficiency are effectively guaranteed.

The invention provides a design method of a two-wheeled wheel-legged robot, which relates to the field of wheeled robots, and comprises the following steps: determining a machine body of the two-wheeled wheel-legged robot, adopting a leg four-bar mechanism as a leg configuration, and taking the leg four-bar mechanism and the machine body as a five-bar mechanism together, a universal leg rod length parameter calculation method is deduced based on a planar five-connecting-rod model, key geometric parameters are determined, and robot structure design is completed; establishing a wheel type motion dynamical model and a leg type jump phase motion dynamical model, deducing a universal motor model selection method, calculating driving wheel and joint motor parameters, and completing wheel train design and whole machine motion simulation; carrying out finite element analysis and lightweight design on the key parts; and finally, an LQR control strategy is adopted, and cooperative control verification is completed through analysis and simulation software. Wheeled efficient movement and leg type terrain adaptive capacity are fused, limitation of a single movement mode is broken through, and the environment adaptive boundary of the robot is expanded.

The invention discloses a multi-target tracking method and related equipment, and the method comprises the steps: carrying out the target detection of a current frame, outputting a corresponding target detection frame result, carrying out the state transition operation through a target motion dynamics model, and generating a motion prediction frame corresponding to each potential target object in the current frame; based on the confidence of a target detection frame contained in the detection frame information, according to the target detection frame and the motion detection frame, generating a corresponding cost matrix, the cost matrix being a generalized symmetric intersection-to-union ratio or a symmetric overlapping score; and inputting the cost matrix into a Hungary matching system, generating an identity label corresponding to the target detection frame, and carrying out association matching on the identity label and the current activation track. According to the embodiment of the invention, the multi-target tracking processing efficiency can be improved, and the robustness, precision and application value of a multi-target tracking system in a complex dynamic environment can be remarkably improved. The method can be widely applied to the technical field of computer vision tracking.

This invention discloses a spacecraft obstacle avoidance control method integrating model predictive control and incremental safety constraints. The method includes: establishing a relative motion dynamics model of the spacecraft and obtaining current state information; generating nominal control inputs based on the current state information using model predictive control; acquiring environmental information within the spacecraft's current local perception range and constructing a local safe state region; generating an expert sample set within the local safe state region through safety reachability analysis and learning local control obstacle functions online; incrementally combining the newly learned local control obstacle functions with existing functions to form a global safety function; and constructing a safety filter based on the global safety function to correct the nominal control inputs, obtaining actual safety control inputs to drive the spacecraft's motion. This invention improves the robustness and real-time performance of obstacle avoidance control by generating dynamic safety constraints in real-time in unknown and complex environments through online learning and incremental combination of local control obstacle functions.

The invention discloses a synchronous orbit electric propulsion satellite short-range game control method based on differential game, which comprises the following steps: under an LVLH coordinate system, establishing a near-circular orbit relative motion dynamic model of spacecrafts of both pursuit and escape parties, and further determining a state space form in a spacecraft pursuit and escape game process; based on a state space form in a spacecraft pursuit game process, introducing a Hamiltonian function, and constructing a short-distance pursuit game model with optimal time; introducing a co-state equation, and describing a short-range pursuit game process as a two-point boundary value problem; and converting the problem into a parameter optimization problem, and carrying out optimization solution on the problem by utilizing an improved difference method so as to realize short-range game control.

The invention discloses a closed-loop unsupervised motion redirection method based on an alternate training mechanism. The method comprises the following steps: S1, an alternate training decoupling stage; s11, defining the motion as three features of a body structure, motion dynamics and a visual angle, and respectively extracting the three features of the source motion by three two-dimensional motion feature encoders with independent parameters; s12, decoupling the extracted features by using an alternate training mechanism based on feature invariance; s2, a three-dimensional motion generation stage; s21, a closed-loop flow is established between the two-dimensional representation and the three-dimensional representation of the motion through rotation and projection, three-dimensional self-supervision constraint is carried out, and decoupled features are recombined; and S22, decoding the recombined motion features into three-dimensional motion representation through a motion feature decoder. Through an alternate training mechanism and a closed-loop self-supervision mechanism, the precision and stability of unsupervised motion redirection are improved.

The present invention sets forth techniques for predicting motion in a 3D model. The disclosed techniques include receiving one or more Gaussian primitives representing a 3D scene and receiving one or more control inputs describing one or more primary motions associated with one or more objects. The techniques also include generating, via a first trained machine learning model, a dynamic state based on the one or more control inputs, and generating, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives. The techniques further include generating a 2D representation of the 3D scene, and generating an output sequence based on the 2D representation, wherein the output sequence depicts both the primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects.

The present application relates to the field of aerospace technology, specifically to a kind of multi-pulse parallel construction method of gravitational wave detection formation considering timing optimization, each spacecraft of formation simultaneously departs from virtual center, each spacecraft is moved to the preset specified position by applying multiple pulses, so as to form a kind of spacecraft formation that can be used for space gravitational wave detection, by giving the steps of calculating the initial position and velocity of each spacecraft and applying initial control to reach the initial position and velocity, the state quantity of each spacecraft at each time in the formation construction process is calculated, so as to provide reference for the design of satellite formation task in actual engineering application, and the accuracy of the linearized relative motion dynamics model established by the multi-pulse parallel space gravitational wave detection formation is improved by differential correction, the error in the relative motion dynamics model is reduced, the initial state set in advance can be effectively reached, and the working efficiency of formation component is improved.

The application discloses a kind of distributed spacecraft in-orbit pursuit game control method and system based on vision, which comprises the following steps: S1, the orbit target image sequence is collected using the forward camera of multiple pursuit spacecraft, and the effective target area is screened out;S2, the feature points are extracted using SuperPoint, and the high-confidence matching point pair is output by SuperGlue structure semantic matching;S3, the intrinsic matrix is calculated and the nine-dimensional state vector is generated by fusing the estimation results of multiple spacecraft;S4, the nine-dimensional state vector is used to establish an orbit pursuit game model, and the optimal control law is solved by combining the relative motion dynamics equation to generate the expert strategy trajectory;S5, the trajectory is used to train the control strategy network;S6, the trained control strategy network is deployed to the attitude and orbit control system, and the orbit control instruction is output to form a perception-decision-control closed loop;The application realizes the efficient cooperative control of distributed spacecraft in-orbit pursuit through spacecraft visual perception, feature matching, game modeling and generative adversarial imitation learning.

The invention relates to the technical field of robot control, in particular to an exoskeleton terrain recognition control method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-sensor data through a plurality of sensors installed on an exoskeleton, carrying out the preprocessing and feature extraction of the data, obtaining terrain geometric features and motion dynamics features, and carrying out the recognition of the terrain; and fusing and reinforcing the two types of features to obtain dynamic enhanced features, inputting dynamic enhanced feature vectors into a preset terrain classifier, outputting a new terrain category, and controlling the exoskeleton to generate an optimal joint control instruction matched with the human body motion gait parameters according to the new terrain category to realize exoskeleton joint control. The problems of terrain misjudgment, control lag, insufficient coupling of terrain information and control instructions and the like caused by dependence on a single sensor are solved, and the self-adaptive power assisting capacity of the exoskeleton under the complex terrain is improved.

The present invention provides a method for coordinated control of an elastic dynamic robust formation based on a control obstacle function, and belongs to the technical field of unmanned system control. The method comprises the following steps: Step S1, establishing a motion dynamics model for each unmanned system, and determining the position information and motion speed of each unmanned system; Step S2, using a control obstacle function to ensure a collision-free safety set between the unmanned system and static obstacles and dynamic unmanned systems, and solving the input safety set of the control system based on forward invariance; Step S3, maintaining the formation based on the control obstacle function; Step S4, performing coordinated control of the formation based on the control obstacle function. The present invention adopts the above-mentioned method for coordinated control of an elastic dynamic robust formation based on a control obstacle function, which can effectively deal with uncertainties and random obstacles in unknown dynamic environments, and improve the coordinated control performance of multiple unmanned systems.

The present invention relates to a design method for reducing the angular velocity of an IMU under a rectangular impact load based on dynamics, and belongs to the field of inertial navigation. In order to solve the problem of reducing the angular velocity of an IMU under a rectangular impact load based on dynamics, the present invention establishes a linear motion dynamics differential equation based on the dynamics model of the IMU under a rectangular impact load; establishes an angular motion dynamics differential equation based on the dynamics model of the IMU under a rectangular impact load; establishes an IMU displacement equation; differentiates the IMU displacement equation to obtain an IMU velocity equation; establishes an IMU angle equation; differentiates the IMU angular velocity equation to obtain an IMU angular velocity equation; calculates the IMU angular velocity, and determines whether the IMU angular velocity meets the design requirements; if it does not meet the design requirements, adjusts the IMU structural parameters and shock absorber parameters until the design requirements are met. The present invention provides support for the structural design of the IMU and the design of the vibration reduction system, thereby improving the efficiency of the IMU structural design.

The present application relates to a kind of multi-unmanned ship fixed time distributed formation control method based on backstepping technique, establishes the dynamic model of unmanned ship movement, and establishes the corresponding fixed time disturbance observer of each follower unmanned ship of pilot unmanned ship, for carrying out online observation and compensation;Further, the backstepping control method enabling multi-unmanned ship to form stable formation in fixed time is constructed, the controller of tracking sub-level system corresponding pilot unmanned ship is constructed by backstepping technique, for controlling pilot unmanned ship to track virtual pilot unmanned ship, and the controller of formation sub-level system corresponding each follower unmanned ship is constructed, for controlling each follower unmanned ship formation to track pilot unmanned ship, the two controllers designed have good convergence, can make the whole formation system stable and reach the expected performance requirement;And preset performance control technique is introduced in design, ensure that formation system still keeps good relative position deviation between unmanned ship under the condition of existing external interference, makes the overall formation configuration stable.

The invention relates to providing motion dynamics estimation. Aspects of the subject technology obtain sensor data from microphones and / or motion sensors in devices disposed on each side of a user's body. The sensor data is used to provide running dynamics estimates including estimates relating to differences between the motion characteristics of the right side of the user's body and the left side of the user's body. These estimates may also be used to provide feedback to the user regarding running style, running route, and injury information.

The invention relates to the technical field of intelligent education, in particular to an AI auxiliary training system for piano playing skill and style analysis, which comprises a multi-modal data synchronization module, an acoustic feature extraction module, a kinematics feature extraction module, a multi-modal feature alignment module, a style matching and deviation analysis module and a personalized training scheme generation module. Performing space-time registration on the audio data and the image data of the piano user to obtain time sequence data; performing high-resolution sound spectrum representation on the playing audio stream to obtain an acoustic skill feature matrix; performing kinematic dynamics reconstruction on the time series data to obtain a kinematic state tensor; performing cross-modal confrontation alignment on the acoustic skill feature matrix and the kinematic state tensor to obtain a composite playing feature tensor; mapping the composite playing feature tensor into a playing style feature library to obtain skill deviation data; and performing directional optimization on the playing style feature library to obtain a personalized correction training scheme. The piano skill improvement efficiency can be improved.

The present application discloses an optimal backstepping formation control method for multi-aircraft based on azimuth measurement, which is used to solve the optimal control problem of multi-aircraft formation under disturbances. This method first establishes a relative motion dynamics model of the aircraft containing disturbance terms, then constructs formation tracking deviation variables that meet the requirements of aircraft position and azimuth constraints. Next, in the design framework of backstepping control, the optimal performance index function for each step is constructed successively. Finally, a deep reinforcement learning algorithm based on the actor-critic structure is introduced to solve the optimal control law and feed it into the relative motion dynamics model of the aircraft to achieve multi-aircraft formation control. The present invention has good practical significance and application prospects in the field of multi-aircraft cooperative formation control.

The invention relates to the technical field of tidal current energy utilization, and discloses an oscillating hydrofoil motion response and fluid-structure interaction deformation solving method, which comprises the steps of defining a hydrofoil motion mode and structure and fluid key parameters, and defining core characteristics of pitching given motion, free heaving motion and bending-torsion coupling deformation; establishing a heaving motion kinetic equation based on the Newton's second law, and constructing a bending-torsion coupling deformation control equation in combination with the structural mechanics principle; building a three-dimensional numerical simulation model containing block grids and boundary layer encryption design, and meeting calculation requirements through spanwise and time discretization processing; solving a fluid distribution load and a total lift force based on a Navier-Stokes equation; and a four-order Runge-Kutta method is adopted to solve the heaving motion response, and a Newmark-beta time integral method and a finite difference method are adopted to solve the bending-torsion coupling deformation. In this way, the strong coupling characteristics of hydrofoil movement and fluid-solid coupling deformation can be accurately captured, and technical support is provided for design optimization and performance improvement of the oscillating hydrofoil type tidal current energy device.

This invention relates to the fields of medical auxiliary diagnosis and computer vision technology, specifically to a multimodal video-assisted diagnostic method and system for Parkinson's disease based on Transformer and attention fusion. First, video data of the subject's hand pinching motion and linear back-and-forth walking motion are acquired and a unified sample label is established. Then, video preprocessing is completed through preprocessing, sliding time window segmentation, and data augmentation. Next, the preprocessed temporal image sequence is input into a spatiotemporal modeling network, where a visual Transformer is used to extract frame-level spatial features. A multi-layer long short-term memory network is then combined to capture long-term temporal motion dynamic information. Finally, a modality-level spatiotemporal attention fusion method adaptively learns modality weights and achieves feature fusion. Finally, the fused features are input into a classification network, outputting prediction results for Parkinson's disease patients or healthy controls. This invention comprehensively characterizes the motion features of the disease through multimodal fusion and accurate spatiotemporal modeling, improving diagnostic accuracy and robustness.

A smart glasses system, smart glasses, and a motion data analysis method. The smart glasses system comprises the smart glasses and a smart terminal connected to the smart glasses. The smart glasses are configured for acquiring motion data of a user by means of detecting the motion of the head of the user. The smart terminal is configured for acquiring the motion data transmitted by the smart glasses, obtaining motion dynamic characteristics of the user according to the motion data, and outputting the motion dynamic characteristics. The described smart glasses system, smart glasses, and motion data analysis method can conveniently and accurately measure the motion dynamic data of the user, thereby improving the accuracy of analyzing the motion dynamic characteristics of the user.

The application discloses an intelligent glasses system, intelligent glasses and a motion data analysis method. The intelligent glasses system comprises the intelligent glasses and a smart terminal connected with the intelligent glasses. The intelligent glasses are used for acquiring motion data of a user by detecting head motion of the user. The smart terminal is used for acquiring the motion data transmitted by the intelligent glasses, obtaining motion dynamic characteristics of the user according to the motion data, and outputting the motion dynamic characteristics. The intelligent glasses system, the intelligent glasses and the motion data analysis method can conveniently and accurately measure motion dynamic data of the user, and improve the accuracy of analyzing the motion dynamic characteristics of the user.

Aspects of the disclosure relate to synthesizing an editable artificial reality scene. Embodiments include a synthesizer tool for generating an editable artificial reality scene. For example, a scene creator may generate an artificial reality scene and publish the scene such that other users may access and / or edit the scene. The artificial reality scene may include scene components such as an avatar, a virtual object, motion dynamics, a background, a camera viewing angle, and the like. Implementations of a permission manager may manage access and / or edit permissions of stored artificial reality scenes. For example, a scene creator may define a range of users where an original scene may be accessed and / or edited, and / or define editable scene components. In some embodiments, multiple users may edit an original artificial reality scene multiple times, thereby generating multiple edited versions of the original scene.

The application provides a pin shaft hole wear life prediction calculation method, comprising the following steps: establishing a suspension assembly motion dynamics model through simulation software, performing suspension dynamics simulation analysis calculation, calculating and extracting the maximum radial force value between the suspension swing arm pin shaft and the mounting seat; analyzing the position of the pin shaft with the maximum stress, performing contact finite element analysis calculation on the suspension swing arm pin shaft and the mounting seat, calculating and extracting the maximum shear stress value of the mounting seat hole; calculating the anti-wear times of the suspension swing arm pin shaft and the mounting seat hole according to the IBM wear calculation method in the tribology principle; calculating the wear life of the suspension swing arm pin shaft and the mounting seat hole according to the suspension natural frequency and the working time length. The application discretizes the continuous wear process, converts the complex dynamic problem into a simple quasi-static problem, has the characteristics of short period, low cost and the like, and can be repeatedly performed, and can effectively shorten the research and development period and reduce the research and development cost.

The invention relates to the technical field of video analysis and processing, and discloses a key frame extraction method based on decoupling multi-task feature learning and related equipment. The method comprises the following steps: performing frame sampling, size normalization and brightness standardization processing on an input video to obtain a standardized frame sequence; inputting the data into a time sequence convolution backbone network, and extracting multi-scale spatial-temporal features based on expansion causal convolution; splitting the features through decoupling branches of motion dynamics, scene composition and object semantics to obtain initial feature factors, and performing independence optimization through a factor separation discriminator to obtain independent motion feature factors, independent scene feature factors and independent object feature factors; encoding the scene description text into a scene vector, calculating the weight of each feature factor through an adaptive attention mechanism, and carrying out weighted fusion; and outputting the importance score of each frame by a multi-layer perceptron, and screening out a target key frame through time sequence non-maximum suppression. According to the method, the key frame extraction efficiency and accuracy are improved.

The application discloses a kind of distributed model prediction control unmanned cluster navigation methods based on homotopy perception, through global path planning and local trajectory optimization;Through the use of the topological structure of environment and the space-time conflict between agents, an optimal path planning algorithm based on homotopy perception is designed, each agent in the unmanned cluster uses a trajectory optimization method based on model prediction control, generates a dynamic feasible and collision-free trajectory, optimizes the motion trajectory of the agent, and improves the global coordination and local motion dynamic adaptability of the agent in the unmanned cluster;Then, through the online replanning strategy, the behavior of the agent in the unmanned cluster is dynamically adjusted in real time from the global and local. The technical scheme of the application can improve the cooperative trajectory planning capability of the unmanned cluster in the dense obstacle environment.

The present application belongs to the field of reusable launch vehicle trajectory planning and intelligent control, and relates to a reusable launch vehicle cruise phase intelligent trajectory planning method. By constructing a three-degree-of-freedom mass center motion dynamics model of the cruise phase launch vehicle, an offline optimal trajectory sample library covering a wide range of working conditions is generated based on the Gauss pseudospectral method, a deep neural network is designed to learn the mapping relationship between task parameters and trajectory features, and high-quality trajectory planning initial values are output online. In combination with the adaptive collocation method, the collocation distribution is dynamically adjusted, and the nonlinear programming problem is iteratively solved with the high-quality initial values as the starting point, thereby realizing efficient and accurate optimization of the trajectory. Simulation results show that the method significantly reduces the sensitivity of online optimization to initial guesses, still has fast response capability and high robustness under complex constraints and uncertain conditions, effectively improves the autonomous trajectory planning capability of the reusable launch vehicle cruise phase, and guarantees flight reliability and control accuracy.

The invention discloses a memory-driven spacecraft inertia estimation method and system and a related product, and belongs to the technical field of spacecraft control. The method provided by the invention comprises the following steps: establishing an attitude motion model containing inertia uncertainty based on a spacecraft attitude motion dynamics relationship; tracking error variables are introduced and reconstructed into an attitude tracking error system motion model; processing the signal by using a fixed parameter linear filtering operator and eliminating a differential term to obtain a first algebraic equation; transforming a regression signal based on the equation to construct an expansion algebraic model; and a historical information accumulation mechanism is introduced in combination with the extended memory regression amount to complete inertia parameter online estimation. According to the method, extra continuous external excitation is not needed, estimation can be achieved only by utilizing the normal attitude motion inherent signals of the spacecraft, meanwhile, the requirement for high-precision speed differential information is avoided, high precision and online real-time performance of inertia estimation are taken into consideration, on-orbit inertia dynamic changes can be effectively tracked, and attitude control stability is guaranteed.

An apparatus includes: a memory configured to store: a first dataset including kinematics data representing multiple human-guided movements of a robot; a second dataset including linguistic descriptors of multiple human-guided movements of the robot; and a processor configured to generate a third dataset based on the first and second datasets, wherein the third dataset includes multiple motion primitives of the robot.

This invention discloses a dual-channel event-triggered unmanned surface vessel (USV) control method with minimum time interval, belonging to the field of USV control technology. To address the communication redundancy problem between USVs and between the controller and actuators, this invention establishes the assumptions of a dual-channel event-triggered USV control method with minimum time interval. Each USV is divided into follower and leader, and the motion dynamics equations for each USV are established. A weighted graph is constructed for the communication topology between USVs. Then, based on the definition of convex hull and the Laplace matrix, an event-triggered position observer with minimum time interval constraints is designed for each USV. An event-triggered tracking controller with minimum time interval is designed for the follower. This invention can significantly reduce the overall system requirement for expensive sensors and reduce the design and implementation costs of multi-USV systems.