41 results about "Kinetic information" patented technology

The invention provides an aerodynamic parameter identification method based on an improved physical information neural network. The aerodynamic parameter identification method comprises the steps of obtaining a target flight state quantity in a flight process of an aircraft; the target flight state quantity is input into the improved physical information neural network, aerodynamic parameter perturbation is obtained, the aerodynamic parameter perturbation is a difference quantized value between a nominal aerodynamic parameter and an aerodynamic parameter in actual flight, and a loss function of the improved physical information neural network is an integral loss function; the integral loss function establishes a relation between a dynamic model containing an estimated value and real dynamic characteristics; and according to the aerodynamic parameter perturbation amount and the corresponding nominal aerodynamic parameter, determining an actual aerodynamic parameter. According to the method, the dynamic model containing the estimated value is associated with the real dynamic characteristics in an integral manner, so that the neural network can learn correct dynamic information in the training process, the accuracy of aerodynamic parameters is improved, and the training difficulty is reduced.

The invention provides a driving anti-slip optimization control method based on vehicle-road cooperation road fusion estimation, and the method comprises the following steps: S1, fusing vehicle-mounted and roadside visual recognition information, and obtaining a vehicle-road cooperation road state recognition result through employing a weighted voting method in combination with road invariant hypothesis and historical information assistance judgment; s2, adaptively adjusting parameters of a dynamic estimator by using a pavement state recognition result, establishing a sample set based on a pavement fusion estimation result, introducing a fuzzy clustering algorithm of particle swarm optimization to realize self-evolution optimization of the structure and the parameters of the estimator, and outputting a fused attachment coefficient estimation result; and S3, designing a slip rate and wheel acceleration combined control strategy to deal with potential slip in advance, and outputting a control torque result of a slip rate controller. Compared with the prior art, the method integrates visual perception and dynamic information to realize accurate estimation of the pavement state, supports advanced intervention of the slip rate controller before slip, and improves the driving stability of the whole vehicle.

The invention generally relates to motion analysis systems and methods of use thereof. In certain aspects, the system includes an image capture device, at least one accelerometer, and a central processing unit (CPU) with storage coupled thereto for storing instructions that when executed by the CPU cause the CPU to receive a first set of motion data from the image capture device related to at least one joint of a subject while the subject is performing a task and receive a second set of motion data from the accelerometer related to the at least one joint of the subject while the subject is performing the task. The CPU also calculates kinematic and / or kinetic information about the at least one joint of a subject from a combination of the first and second sets of motion data, and outputs the kinematic and / or kinetic information for purposes of assessing a movement disorder.

The invention provides a robot control method and device, computer equipment and a storage medium, and relates to the technical field of computers. According to the method, joint driving torque information of a robot is introduced, and a force sense condition mark corresponding to a noise adding action sequence is constructed as a condition constraint in a diffusion denoising process, so that the generated target action sequence can explicitly consider force sense and dynamics information, and the noise is reduced. And the stability and safety of the robot in physical interaction tasks such as contact, grabbing and assembling are effectively improved.

The invention relates to the field of metallurgical casting, in particular to a phase field dynamics method for simulating precipitation evolution of a gamma'phase in nickel-based superalloy under external stress. The method comprises the following steps: S1, acquiring thermal and dynamic information of a gamma phase and a gamma'phase by adopting a CALPHAD method according to target components of the nickel-based superalloy; s2, constructing a strain energy item containing plastic strain, and defining stress-free strain generated by lattice mismatch during gamma-to-gamma 'phase transformation and a gradient energy item capable of considering different slip system information; s3, establishing a phase field model, solving a phase field control equation at a two-phase interface by adopting a Kim-Kim-Suzuki interface model to obtain a sequence parameter result value, and investigating the influence of different forms of external stress loads and different plastic strains on gamma'phase precipitation growth; and S4, the gamma'phase precipitation evolution structure in the nickel-based high-temperature alloy is subjected to visualization treatment, and the influence rule of the external stress and the plastic strain on gamma 'phase precipitation evolution is clarified.

The application provides an aerodynamic parameter identification method based on an improved physical information neural network, comprising: obtaining a target flight state quantity in a flight process of an aircraft; inputting the target flight state quantity into the improved physical information neural network to obtain an aerodynamic parameter perturbation quantity, the aerodynamic parameter perturbation quantity being a difference quantitative value between a nominal aerodynamic parameter and an actual aerodynamic parameter in flight, a loss function of the improved physical information neural network being an integral loss function, the integral loss function establishing a connection between a dynamic model containing an estimated value and a real dynamic characteristic; and determining the actual aerodynamic parameter according to the aerodynamic parameter perturbation quantity and the corresponding nominal aerodynamic parameter. Through implementation of the application, the dynamic model containing the estimated value is connected with the real dynamic characteristic in an integral manner, correct dynamic information is ensured to be learned by the neural network in a training process, the accuracy of the aerodynamic parameter is improved, and the training difficulty is reduced.

The invention provides a road adhesion coefficient estimation device and method based on vision and dynamics information fusion. The method comprises the steps of vehicle dynamics estimation based on extended Kalman filtering, road type identification based on a lightweight convolutional neural network and a fusion strategy of vision and dynamics information. According to the pavement type identification method based on the lightweight convolutional neural network, efficient and accurate visual information processing is realized; in combination with dynamic response information, space-time synchronization and fusion estimation of vision and dynamic data are realized, and predictability and accuracy of road adhesion coefficient estimation are improved; in addition, real-time and reliable adhesion coefficient input is provided for an active safety system, and the vehicle safety and stability in a complex scene are remarkably improved.

The present application relates to a kind of device and method for measuring solid-liquid two-phase flow dynamics information, including measuring plate, glass plate, pore pressure sensor, filter screen, triaxial force sensor, force plate, outer pressure strip, inner pressure strip, sealing film, sensor bracket, dynamics information includes the pressure of liquid component in two-phase flow and the force of overall to force plate;The present application can ensure fluid seal when the mechanical sensor is reasonably measured dynamics information;Meanwhile, the present application can also realize the simultaneous measurement of solid-liquid two-phase flow kinematics and dynamics information by mechanical sensor cooperation high-speed camera technology.

The invention relates to a train spacing determination method and device, computer equipment and a storage medium, and belongs to the technical field of railway transportation. The method comprises the steps that dynamics information and positioning measurement information of adjacent trains are acquired; wherein the adjacent trains comprise the first train and the second train. And according to the dynamic information and the positioning measurement information, a first interval and a second interval between the adjacent trains are determined. And determining the train spacing between the adjacent trains according to the first spacing and the second spacing. According to the method, when the distance between the adjacent trains is determined, the positioning measurement information of the adjacent trains is considered, the dynamic information of the adjacent trains is also considered, and compared with a traditional method for determining the distance between the adjacent trains only by means of a satellite positioning system, the method is more comprehensive in consideration, and the accuracy of the distance between the adjacent trains is improved. And the obtained train distance between the adjacent trains is more accurate.

This invention discloses a simulation method and related products for the influence of irradiation defects on the micromechanical properties of Zr-Nb alloys. The simulation method includes the following steps: S1, obtaining Zr-Nb alloys containing typical irradiation defects and obtaining stable configurations of Zr-Nb alloys containing typical irradiation defects based on classical molecular dynamics; S2, calculating the micromechanical properties of the stable configurations obtained in step S1 by simulating a nanoindentation experiment, and obtaining the micromechanical properties of each system; S3, based on the micromechanical properties obtained in step S2, and combined with the kinetic information of the internal microstructure changes, establishing the correspondence between micromechanical properties and microstructure. This invention, through this simulation method, can establish the correspondence between micromechanical properties and microstructure, helping to reveal the influence of irradiation defects on their micromechanical properties.

The invention discloses a human-machine interaction control method based on a logarithmic sliding mode observer, which consists of the following steps: step S1: obtaining dynamic information of human-machine interaction, performing data processing, and calculating the control law of the human-machine interaction process; step S2: calculating the rate of change of the adaptive law according to the data in step S1, and updating the adaptive gain; step S3: calculating the operation behavior estimation of the current cycle according to the observer algorithm and the control law in step S1; step S4: updating the adaptive observation parameters according to the operation behavior estimation in step S3, completing the closed-loop iteration of the algorithm, and ensuring the stability of system tracking control and operation behavior estimation under actual limited time conditions; the invention avoids the necessity of assembling expensive force / torque sensors in human-machine interaction, can quickly respond to the reference trajectory generated by the operation behavior, and at the same time ensures high steady-state accuracy, ensuring that the task process fully reflects the control intention of the operator.

The invention provides a terrain recognition method, which belongs to the field of intelligent driving and comprises the following steps: acquiring visual data, dynamic data and weather and vehicle working condition data; inputting the visual data into the recognition model to obtain a visual terrain scoring result, and inputting the dynamic data into the recognition model to obtain a dynamic terrain scoring result; inputting weather and vehicle working condition data into a recognition model to obtain a weight coefficient; according to the scheme, a vision and dynamics fusion method is used for identifying the terrain, and the defect that a single information source scheme is limited by weather or working conditions is overcome. Visual information and dynamic information are subjected to weighted fusion, weight coefficients are obtained through training according to factors such as weather and working conditions, the adaptive capacity is achieved, the difficulty of manually setting threshold values and weights is avoided, and the misrecognition proportion is reduced.

The invention belongs to the technical field of intelligent traffic systems, and discloses an expressway truck transverse oscillation amplitude prediction method based on trajectory data, which fuses high-frequency floating truck data, road design data and truck driving dynamics information, and obtains trajectory data through data preprocessing and abnormal data elimination. Extracting a vehicle transverse oscillation index from the trajectory data, selecting a plurality of independent variables, judging the influence relationship of the independent variables on the vehicle transverse oscillation index based on a hierarchical regression method to obtain significant influence independent variables, and finally constructing a prediction model of the significant influence independent variables and the transverse oscillation amplitude based on a Gaussian regression process. The method effectively solves a problem that the modeling capability of the lateral shift behavior of the truck is insufficient in an existing method, and improves the scientificity and robustness of prediction.

The application discloses a kind of robot optimal human-computer interaction impedance control method based on joint learning, storage medium and robot, method includes the following steps: constructing task space reference regression trajectory, human-computer interaction second-order impedance model and task space auxiliary trajectory, establish human-computer interaction task space augmented system and corresponding evaluation index function;Based on integral reinforcement algorithm, human-computer interaction second-order impedance model parameters are updated online until optimal parameters are obtained;For second-order impedance model, construct adaptive neural network impedance controller;Based on the theory of deterministic learning, experience knowledge is obtained, and a constant neural network impedance controller is constructed.This method effectively solves the accurate impedance control of human-computer interaction under the condition of unknown dynamics information and unknown human motion characteristics of robot, and realizes the online optimal adjustment of human-computer interaction parameters under different task scenarios and different interactive objects by using reinforcement learning mechanism.

The invention provides a robot safety control method based on fault-tolerant control, which is applied to a robot control system comprising a multi-degree-of-freedom joint driver and a tail end executing mechanism, and comprises the following steps: constructing a state observer based on a robot model, and comparing by combining joint measurement data and a redundant sensor to obtain fault state information; the space relation and the movement speed of the person and the robot are collected, fault state information and space and dynamics information are fused, and a comprehensive safety risk index is calculated; according to comparison between the index and a multi-gear risk threshold value, the function safety state machine is driven to be switched among multiple operation states; the motion parameters are subjected to amplitude limiting in the amplitude limiting operation state, constraint is applied to the fault joint in the degradation operation state, and a joint control instruction meeting the safety constraint is generated based on the residual degree of freedom. According to the invention, the robot can be controlled to decelerate to stop in a safe shutdown state, and graded safety protection and safe operation with faults under a fault working condition are realized.

The application provides an interception guidance strategy acquisition method and device, equipment and a storage medium. The method comprises the following steps: obtaining a first Bellman equation, which is an off-policy integral reinforcement learning Bellman equation obtained based on a nonlinear interception dynamics equation, a game value function and an iterative control strategy; obtaining control input data and state output data; and iteratively solving the first Bellman equation based on the control input data and the state output data to obtain an optimal interception guidance control strategy. In this way, the optimal control strategy is found in a data-driven manner without relying on specific dynamics information, so that the optimal control strategy is used to achieve the purpose of high-precision interception of a high-maneuvering escape party by a pursuit party.

The invention relates to the field of power load fluctuation prediction, and provides a power load prediction method and system based on dynamic driving. The power load prediction method based on dynamic driving comprises the following steps: performing dynamic modeling on an obtained historical power load time training sequence by adopting a deterministic learning algorithm, and extracting internal dynamic information of data; on the basis of dynamic information, a multi-step forward iteration predictor based on a fourth-order Runge-Kutta model is adopted, and a power load prediction sequence composed of prediction values from the step 1 to the step 2 is obtained; a corrector is adopted to perform periodic correction on a power load prediction sequence formed by the prediction values from the step 1 to the step 2, and a power load prediction sequence correction value of the step 1 is obtained; and obtaining a power load prediction result based on the prediction value and the correction value. And the power load prediction precision is improved.

This invention belongs to the field of robot path planning and discloses a ground robot path planning method based on deep reinforcement learning. Based on deep reinforcement learning and combined with a robot dynamics model, it aims to propose a safe and reliable path in a field environment. The method first constructs a 3D scene and preprocesses it, then performs dynamic modeling of the robot to build a deep reinforcement learning model. This model uses the Asynchronous Advantage Actor-Critic method to learn the optimal path planning strategy and the optimal state value function, designs a reward function considering terrain and distance, and designs an exploration strategy based on the interaction between the robot and different terrains. Finally, a deep convolutional neural network model is trained to drive the robot to obtain the maximum reward value during operation. This method comprehensively considers terrain and robot dynamics information to plan a safe and reliable path for the robot in a 3D scene.

The present invention refers to a method for estimating an intrinsic speed of a vehicle using a vehicle radar system with at least one radar sensor (4) for monitoring the surroundings of the vehicle (1), the method involving transmitting a radar signal via a transmission antenna of the radar sensor, receiving one or more reflected wave signals via a receiving antenna of the radar sensor, the reflected wave signals being the radar signal reflected by one or more objects, calculating an unambiguous Doppler velocity (VD), wherein the unambiguous Doppler velocity (VD) is calculated by the measured ambiguous Doppler velocity (VD,CoG) and a Doppler hypothesis (VDHypo), calculating a host-kinetic information (κ) of the vehicle (1) by using the unambiguous Doppler velocity (VD), determining an estimation of the intrinsic speed of the vehicle from the host-kinetic information (κ).

The invention provides a fixed time control method for a flexible double-connecting-rod mechanical arm, which is applied to the field of robot intelligence and comprises the following steps: establishing a flexible system dynamics model with dead zone input; deducing an initial control moment depending on the flexible system dynamic model with dead zone input by combining a backstepping method; designing a first radial basis function neural network update rate based on unknown dynamic information in the radial basis function neural network approximate initial control torque; designing a second radial basis function neural network update rate based on a radial basis function neural network approximation unknown dead zone function; the fixed time control torque of the novel flexible double-connecting-rod mechanical arm is obtained on the basis of the two radial basis function neural network update rates and the initial control torque, the fixed time control strategy provided by the invention ensures rapid convergence of the flexible double-connecting-rod mechanical arm, and even under the operation condition involving system constraints, the control torque of the flexible double-connecting-rod mechanical arm can be effectively controlled. And the trajectory tracking precision and the vibration suppression performance can be improved at the same time.

The invention discloses a simulation test method and system for a parking function. The method comprises the following steps: respectively deploying an automobile virtual test simulation platform, a Simulink model and a test code; selecting a required parking test scene from the automobile virtual test simulation platform, and providing parking test scene parameters; reading environment information and vehicle position information by using the test code; a Simulink model is used for reading an interaction control instruction and vehicle body dynamics information; the test code and the Simulink model perform information interaction and cooperative control according to the environment information, the vehicle position information, the interaction control instruction and the vehicle body dynamics information; and the Simulink model generates a parking control instruction and sends the parking control instruction to the automobile virtual test simulation platform so as to realize a closed-loop test of the parking function according to the test case. According to the invention, information interaction and cooperative control can be carried out on the test code and the Simulink model, and the actual parking process can be simulated more accurately.

The present invention discloses a vehicle load calculation method and a vehicle, comprising: continuously obtaining an enable signal of a power output device and a vehicle speed after the vehicle is powered on; obtaining the current working condition information of the vehicle when the vehicle speed is greater than zero and the enable signal is not obtained; continuously obtaining the dynamic information of the vehicle when the working condition information first meets the enable condition for load calculation; continuously calculating the estimated load of the vehicle according to the dynamic information based on a recursive least squares method; obtaining the cumulative time after the vehicle is powered on that the working condition information meets the enable condition for load calculation; determining the actual load of the vehicle according to the cumulative time and the estimated load; obtaining a preset load range of the estimated load according to the actual load; obtaining the sum of squares of standard errors of the recursive least squares method according to the estimated load and the preset load range within a preset time period; and after obtaining the sum of squares of the standard errors, determining the actual load of the vehicle according to the estimated load and the sum of squares of the standard errors.

The invention provides an interception guidance strategy obtaining method and device, equipment and a storage medium, and the method comprises the steps: obtaining a first Bellman equation which is a separation strategy integral reinforcement learning Bellman equation obtained based on a nonlinear interception kinetic equation, a game value function and an iteration control strategy; acquiring control input data and state output data; and iteratively solving the first Bellman equation based on the control input data and the state output data to obtain an optimal interception guidance control strategy. Thus, under the condition of not depending on specific dynamic information, the optimal control strategy is found in a data driving mode, so that the purpose that the pursuit party performs high-precision interception on the high-maneuverability escape party is achieved through the optimal control strategy.

The application discloses a differential dynamic microscopic image acquisition processing method based on a phase spectrum and a related device, relates to the technical field of particle microscopic image processing, and can measure kinetic information such as a diffusion coefficient of Brown particles. Compared with traditional microscopic image technology, the application is a new technology combining light scattering and optical microscopy. The experimental equipment used in the application is simpler, and the experimental steps are fewer; the application is more convenient to combine with other technologies; the application can well solve the sample concentration limitation problem; and the application can measure a wider variety of particles. The method is to use a high-speed camera to shoot time sequence nanoparticle Brown motion microscopic images, then calculate phase changes of two images with different time intervals through a cross-correlation function, find the relationship between the data obtained in the previous steps and the diffusion coefficient, and thus obtain the diffusion coefficient and other kinetic information of the Brown particles.

The invention discloses an extended target tracking and shape estimation method based on rectangular geometric constraint, which adopts the idea of rectangular geometric constraint and comprises the following steps of: firstly, modeling the shape of an extended target into a rectangle, and obviously reducing the state space complexity by only retaining two parameter dimensions, namely length and width; then, angle areas where long sides and short sides are located are divided according to diagonals of the rectangle, and a radial function meeting strict rectangular geometric conditions is solved in each area through a trigonometric function; combining the radial function, the dynamic information of the target, the observation function of the sensor and the noise statistical characteristics to construct a measurement equation with rectangular geometric constraints; and finally, embedding the measurement model subjected to the rectangular geometry constraint into an unscented Kalman filter to realize joint estimation of the dynamic state and the rectangular shape of the extended target. According to the method, strict shape estimation of the rectangular extended target is realized, and the accuracy and the stability of estimation of the dynamic state and the shape of the rectangular extended target are remarkably improved.

The application discloses a kind of machine dog joint gap online compensation method and system, it is related to machine dog joint control technical field.A kind of machine dog joint gap online compensation system, including have: machine dog movement monitoring module and machine dog movement compensation module.The application obtains joint dynamics information and foot end dynamics information by dynamic, can real-time monitoring machine dog in complex environment Motion state, to provide accurate basis for subsequent compensation;By dynamics topological decoupling to machine dog, complex multi-joint system is decomposed into multiple independent joint control unit, improve control accuracy and response speed, make system more stable under different load conditions;By real-time distinguishing joint is in clearance stagnation period or rigid engagement period, can dynamically adjust compensation strategy, different compensation measures are taken for different motion state, to significantly improve motion accuracy and stability.

The present disclosure relates to a vehicle control apparatus and a method thereof. The present disclosure may include a sensor, such as light detection and ranging (LiDAR), and a processor. The processor may generate a first bounding box corresponding to an external object based on contour points representing a shape of the external object, generate a second bounding box based on aligning first vertices included in the first bounding box around a reference axis, determine whether the first bounding box or the second bounding box indicates that the external object does not interfere with an operation region of the vehicle, based on the contour points, second vertices included in the second bounding box, and dynamics information of the external object.

The present disclosure provides a robot arm trajectory optimization method, device, electronic device and storage medium, which relates to the field of robot arm technology. The specific implementation scheme is: according to the dynamic information of the robot arm, determine the first objective function and the second objective function of the robot arm; according to the joint angle information and the angular velocity information of the robot arm, determine the constraint conditions of the robot arm; according to the first objective function, the second objective function and the constraint conditions, determine a multi-objective function with the minimum base disturbance as the high priority and the minimum torque energy of the robot arm as the secondary priority; optimize the multi-objective function based on the chaotic particle swarm algorithm to obtain the optimal solution information of the multi-objective function, so as to avoid the problem of premature convergence and falling into the local optimal solution of the particle swarm algorithm. According to the optimal solution information of the multi-objective function, determine the motion trajectory information of the robot arm, so as to provide the robot arm with the expected trajectory that meets the multi-objective function, so as to improve the actual application effect of the robot arm.

The invention discloses a load calculation method for a vehicle and the vehicle, and the method comprises the steps: continuously obtaining an enable signal of a power output device and the speed of the vehicle after the vehicle is powered on; when the vehicle speed is greater than zero and the enable signal is not obtained, obtaining current working condition information of the vehicle; when the working condition information meets the load calculation enabling condition for the first time, dynamic information of the vehicle is continuously obtained; continuously calculating the estimated load of the vehicle according to the dynamic information on the basis of a recursive least square method; obtaining the accumulated time when the working condition information meets the calculation enabling condition of the load after the vehicle is powered on; determining the actual load of the vehicle according to the cumulative time and the estimated load; acquiring a preset load range of the estimated load according to the actual load; according to the estimated load within the preset time period and a preset load range, obtaining a standard error sum of squares of a recursive least square method; after the standard error sum of squares is obtained, the actual load of the vehicle is determined according to the estimated load and the standard error sum of squares.