346 results about "Quaternion" patented technology

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention relates to an all-humanoid robot motion redirection method and device based on motion capture equipment, and the method comprises the steps: converting a human body posture quaternion collected by the motion capture equipment into a rotation matrix, and achieving the precise mapping from a Cartesian space to a robot joint space in combination with an Euler angle decomposition strategy; a quaternion-disassembled joint angle is used as an initial solution, various physical constraints are introduced in real time according to the actual supporting state of the robot, and the lower limb joint angle is corrected online by constructing and solving an inverse kinematics problem with various constraints, so that the robot reproduces the action of an operator, and the joint angle of the lower limb can be corrected online. And the requirements of joint limiting, self-collision avoidance, dynamic balance and the like are strictly met, so that the accuracy of motion simulation and the stability of the system are effectively unified.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

The invention relates to the technical field of attitude determination of a photoelectric pod of an unmanned aerial vehicle, in particular to an MEMS-IMU dual-mode correction attitude determination method for the photoelectric pod of the unmanned aerial vehicle. Comprising the following steps: establishing a damping system second-order transfer function model, and outputting an attenuation coefficient matrix; based on MEMS-IMU three-axis motion parameters, distinguishing a large maneuver turning state and a linear flight state; respectively compensating a roll angle, a pitch angle and a course angle of the MEMS-IMU by using the main inertial navigation according to different states; converting the corrected attitude angle into a quaternion, and optimizing and outputting a fused quaternion through a gradient descent method; taking the fused quaternion as an input, constructing a seven-dimensional state space model, and outputting an error compensation amount through Kalman filtering; dynamically adjusting the main inertial navigation weight according to the accelerated speed root mean square; and outputting a final attitude angle in combination with the error compensation amount and the weight, and verifying the final attitude angle through GNSS (Global Navigation Satellite System) position inversion. The method has the advantages that cross interference is avoided; therefore, the method is suitable for the photoelectric pod scene of the unmanned aerial vehicle.

The invention discloses a learning-based composite layered anti-interference control system and method suitable for a large-windward-area unmanned aerial vehicle, is used for realizing robust wind resistance control of the large-windward-area unmanned aerial vehicle, and aims at the flight characteristics of large windward area and sensitivity to external wind field change in a vertical take-off and landing stage. A wind speed estimation method without an additional sensor is provided, modeling of external disturbing force is realized in combination with a Gaussian process regression algorithm, and compensation of the disturbing force is realized by using quaternion-based model prediction control, so that the influence of an external change wind field on the dynamics of the large-windward-area unmanned aerial vehicle is reduced, and the trajectory tracking precision is remarkably improved. A backstepping controller based on SO (3) is designed in an attitude control loop, a nonlinear disturbance observer is integrated, and active compensation of external disturbance torque and robust tracking control of attitude are realized. Based on the technical characteristics, a complete dynamic control link capable of estimating and feeding back compensation disturbance in real time is further constructed.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention discloses a manned aircraft / unmanned aerial vehicle pursuit deep learning control method simulating a pigeon positive and negative feedback mechanism. The method comprises the following steps: step 1, giving mathematical models of a manned aircraft and an unmanned aerial vehicle and performing environment initialization; step 2, building a quaternion PID controller of the unmanned aerial vehicle; 3, a manned aerial vehicle / unmanned aerial vehicle hierarchical decision-making architecture is established; step 4, mathematical modeling of a pigeon positive and negative feedback mechanism; 5, designing a manned aerial vehicle / unmanned aerial vehicle cluster reward function simulating positive and negative feedback of the pigeon flock; 6, planning and designing an escape unmanned aerial vehicle path; step 7, predicting an escape unmanned aerial vehicle path; and 8, generating a manned aerial vehicle / unmanned aerial vehicle cluster pursuit learning strategy based on the actor-commentator network. According to the invention, an efficient, flexible and extensible bionic intelligent pursuit control framework is constructed, and the pursuit task execution efficiency and cooperative combat capability of the manned aerial vehicle / unmanned aerial vehicle heterogeneous cluster in a complex dynamic environment are improved.

The invention relates to the field of array signal processing, and particularly discloses a DOA joint estimation method based on a quaternion polarization sensitive array. Electromagnetic wave dual polarization components are captured through the orthogonal dipole and the loop antenna, and a quaternion observation matrix is constructed; calculating a quaternion covariance matrix by adopting a sliding window mechanism, and separating a signal / noise subspace by adopting quaternion singular value decomposition; and constructing a spatial spectrum function in combination with a quaternion steering vector, and realizing joint estimation of an azimuth angle and a pitch angle through two-dimensional search. According to the method, the unified characterization capability of quaternions on polarization-airspace information is fully utilized, and the DOA estimation precision and the anti-interference performance of the multi-polarization signal are remarkably improved.

The invention discloses a positioning and attitude determination method and device integrating Beidou navigation and inertial navigation, and relates to the technical field of attitude measurement, the method comprises the following steps: correcting a gyroscope error by using an accelerometer error and a magnetometer error based on proportional integral operation to obtain a gyroscope output after error correction; using the gyroscope output after error correction to update the quaternion of the moving object, using the quaternion to calculate the attitude, and obtaining the attitude angle of the moving object under the carrier coordinate system calculated through inertial navigation; moving object positioning and attitude determination calculation is carried out by using the time difference of the satellite carrier phase double differences between the double BDS receivers and the different groups of satellites, and the attitude angle of the moving object under a carrier coordinate system calculated through Beidou satellite navigation is obtained; and performing Kalman filtering fusion on the attitude angle calculated by inertial navigation and the attitude angle calculated by Beidou satellite navigation to obtain a final attitude angle. The method can adapt to a dynamic environment, and the attitude measurement precision is improved.

The invention relates to the field of industrial robot automatic grabbing, and discloses a swivel nut robot grabbing system and method.The system comprises an image acquisition module, a background segmentation module, a feature fusion module, a pose regression module, a position calculation and judgment module, a motion execution module, a synchronous triggering module and a safety control module; processing to generate a point cloud; extracting CNN texture and PointNet + + geometric features of the point cloud, and splicing and outputting fusion features; the three-dimensional coordinates and the quaternion rotation amount of the key points are regressed and converted into six-dimensional poses, accessibility is judged, and effective poses are output; and the six-axis mechanical arm controller receives the signals and then executes grabbing. The camera is triggered to collect when the thread sleeve enters the visual field, the pose confidence coefficient is monitored in real time, and the servo power supply is cut off when the pose confidence coefficient is low. The position and posture of the thread sleeve can be accurately obtained so as to control the six-axis mechanical arm to grab, through multi-module cooperation, an advanced algorithm and real-time monitoring, the grabbing accuracy and safety are improved, and the operation risk is reduced.

The invention discloses a magnetic positioning resolving method and system, and relates to the technical field of magnetic positioning and sensor resolving, the method successfully separates an attitude error from magnetic field drift by explicitly introducing a rotation quaternion and bias modeling, has good physical interpretability and environmental adaptability, and can be applied to the field of magnetic positioning and sensor resolving. The precision, the robustness and the generalization ability of the magnetic positioning system in a complex environment can be remarkably improved, and the method is suitable for various application scenes such as robot navigation, industrial detection and medical equipment.

The invention discloses an intelligent processing method and system for multi-source data fusion of surveying and mapping equipment, and belongs to the technical field of surveying and mapping data processing.According to the method and system, an initial LiDAR point cloud is combined with position data and attitude data for joint calculation to obtain an accurate LiDAR point cloud, the accuracy of point cloud data is optimized, and therefore the accuracy of final registration is improved, and the registration accuracy is improved. And matching the point cloud multi-scale feature point set with the image multi-scale feature point set to obtain a point cloud homonymy point set and an image homonymy point set, analyzing a rotation matrix and a translation vector through a quaternion registration algorithm, and completing registration of the target panoramic image and the precise LiDAR point cloud according to the rotation matrix and the translation vector. And high-precision space registration is realized through a quaternion registration algorithm, the registration accuracy is further improved, and the surveying and mapping precision is improved.

The invention belongs to the field of seat control, and particularly relates to a seat control method based on multi-modal data fusion in order to solve the technical problems that in the prior art, model interpretability is poor, and adjustment logic and physiological comfort requirements of a human body are inconsistent, and the seat control method comprises the following steps that S1, after a first feature vector and a second feature vector are mapped to a quaternion space, the first feature vector and the second feature vector are mapped to the quaternion space; generating an initial interaction tensor by calculating the Hamiltonian product of the two; s2, using KL divergence between prior distribution and empirical distribution as a regular term for constraint, and resolving a human body state vector; s3, inputting the human body state vector into the generative network to obtain a fusion feature vector; and S4, based on the fusion feature vector, calculating a control signal used for adjusting the seat surface posture of the seat, the protrusion amount of the waist supporting structure and the clamping angle of the side wing wrapping structure. According to the invention, the analysis and fusion of the vehicle movement trend are realized, so that the seat control can fully foresee and cope with the influence of the vehicle movement on the driver and passengers.

The invention provides a dynamic walking exoskeleton control method and system based on man-machine complementary optimization, and the method comprises the steps: defining the step height and normal contact force of a contact point, and constructing a nonlinear complementary problem constraint and model; defining quantitative targets of nine types of motion tasks including contact point centroid position tracking, centroid linear velocity tracking, foot yaw angle alignment, frame attitude tracking, base quaternion derivative regularization, contact force regularization, joint regularization, swing height control and contact control regularization, and establishing a multi-task target function; a static nonlinear complementary problem is coupled with exoskeleton continuous dynamics, and a coupling system is constructed; solving the optimization objective function by adopting a neurodynamics algorithm to obtain the optimal control input of the exoskeleton-human body system; and the control input mapping obtained through optimization is converted into a driving instruction of a robot actuator, and a motion control closed loop is completed. According to the method, the complex movement requirements under strong coupling constraint interaction of the humanoid and the robot can be more accurately met.

The invention discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping a flow time sequence feature into a quaternion tensor to maintain an internal coupling relationship of a multi-dimensional feature; a double-flow encoder is designed, a quaternion selective state space model is adopted to extract continuous fluid features, and a dynamic hypergraph neural network is adopted to model discrete protocol features; carrying out self-supervised pre-training on a resistance pseudo-anomaly sample by utilizing potential diffusion model generation, and optimizing characterization by combining quaternion cepstrum distance loss; the injected learnable prompt vector is optimized in the small sample fine tuning stage, and a category prototype is corrected by using a semi-supervised expectation maximization algorithm; and calculating a sample anomaly score based on an energy model to realize known attack classification and unknown anomaly judgment. According to the method, the generalization ability of the model under the small sample condition and the unknown threat detection ability are remarkably improved.

The invention relates to a vector seismic data random noise suppression method and device based on fast quaternion curvelet transformation, a medium and equipment, and the method comprises the steps: representing X, Y and Z as a quaternion array form Q = Xi + Yj + Zk in a time-space domain, and forming a two-dimensional quaternion signal f; according to the data features, fast quaternion curvelet forward transformation is used for decomposing f in Ncsalt scales and Ncoarse directions, and a corresponding quaternion curvelet transformation coefficient c is obtained; estimating thresholds under various scales and directions, performing threshold operation on c of different scales and different directions to obtain a quaternion curvelet transform coefficient after threshold processing, and performing fast quaternion curvelet inverse transformation to represent fast quaternion curvelet inverse transformation; reconstructing the denoised quaternion signal, taking out three corresponding components from three imaginary part positions of the denoised quaternion signal, completing vector denoising, and obtaining a denoising result sum;

The invention discloses a robot control system and method based on AI voice, and belongs to the technical field of robot intelligent control. The system comprises a local voice processing module, a dialogue management and context understanding module, an instruction mapping and generating module and a robot communication and control module. The local voice processing module is integrated with a 16 kHz sampling ASR engine, voice-to-text conversion is realized through a 6-layer LSTM network and beam search decoding, and the TTS engine adopts a WaveNet architecture to synthesize voice. A 7 billion parameter large language model is arranged in the dialogue management module, a 32-layer Transform decoder architecture is adopted, 10 rounds of dialogue history is maintained, and intention recognition and entity extraction are achieved through a multi-head self-attention mechanism. And the instruction mapping module performs trajectory planning by adopting quintic polynomial interpolation and quaternion spherical linear interpolation. According to the invention, full-link localization processing is realized, the speech recognition delay is 180 milliseconds, the position error is less than 0.5 mm, and the problems of high delay, high privacy risk and weak complex instruction understanding ability in the prior art are effectively solved.

The invention provides an unmanned aerial vehicle control-oriented TCN-LSTM series network multi-source state real-time sensing method and system. The method comprises the following steps of: constructing a deep neural network model formed by connecting a TCN and an LSTM in series; the speed, attitude and position information of the unmanned aerial vehicle is acquired through cooperation of the RTK-GPS, the INS and the IMU, and time alignment is carried out on IMU data and RTK-GPS / INS data by using an interpolation method; the collected data is divided into a training set, a verification set and a test set, and a composite loss function fusing RMSE, MAE, quaternion angle errors and offset errors is adopted to train and evaluate the model; serializing sensor data based on a sliding time window strategy to form an input feature vector, and standardizing IMU data according to channels; and inputting the standardized sensor data into the TCN-LSTM model, and outputting a state estimation result for navigation, path planning or closed-loop control.

The invention relates to the technical field of attitude estimation, in particular to an attitude estimation method in a magnetic interference environment, which comprises the following steps of: obtaining a conversion relation between an Euler angle and a quaternion; calculating the quaternion at the next sampling moment; respectively solving a first state transition matrix, a first process noise matrix and a first prior covariance according to the first state equation; respectively solving a second state transition matrix, a second process noise matrix and a second prior covariance according to a second state equation; elements in the nb-series rotation matrix are used as observed quantities of state variables in the parameter-series rotation matrix, a corresponding Kalman gain is calculated, and a Jacobian matrix is designed by using the observed quantities; calculating an observation noise covariance matrix by using the Jacobian matrix; updating the Kalman gain by using the observation noise covariance matrix; and updating the state equation and the posterior covariance by using the updated Kalman gain and the observed quantity. The method solves the problem of how to prevent the calculation of the roll angle and the pitch angle from being influenced by magnetic interference when the magnetic interference exists.

The invention discloses a method for generating a high-precision dynamic digital human, which belongs to the field of computer vision and comprises the following steps of: extracting skeleton motion and frame-level potential codes from a multi-view video, driving a coarse model network to generate a three-dimensional grid, and realizing attitude alignment by combining double quaternion skin and embedded deformation; a vertex three-dimensional corresponding relation is obtained through 2D point tracking and a depth map, and grid alignment precision is optimized; on the basis, animatable Gaussian textures are trained, and texture detail modeling is achieved; a physical solver is further introduced, the physical response of cloth is solved according to the bone posture and the material type, and die penetration and distortion are eliminated; meanwhile, modeling is carried out on texture offset by adopting a space-time Transform, so that the dynamic consistency is improved; and finally, high-fidelity image rendering is completed through analytical sputtering, and a dynamic digital human which is rich in details, stable and controllable is generated.

The invention discloses an indoor unmanned aerial vehicle path planning method, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: constructing an indoor Euclidean symbol distance site map of an unmanned aerial vehicle, and generating an initial collision-free discrete path point sequence; constructing a safe flight corridor based on the discrete path point sequence, and generating a control point grid; in combination with a q-calculus theory, calculating a curve tension coefficient q based on a control point grid, and generating a three-dimensional space position path trajectory by using a tensor product q-Bezier curved surface equation; the geometric curvature of the three-dimensional space position path trajectory is calculated, shape parameters are calculated, and a continuous attitude path is generated according to the shape parameters by using a parametric quartic unit quaternion spline interpolation formula; and generating a complete flight path control instruction sequence. According to the method, in a complex indoor environment, a flight path which is highly safe, free of collision, extremely stable, free of oscillation and free of sudden change of angular acceleration is generated, and the flight safety and control quality of the unmanned aerial vehicle are remarkably improved.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

The invention relates to the technical field of intelligent robot attitude sensing and dynamic monitoring, and discloses a five-axis robot attitude monitoring method based on an intelligent sensor. Global and local coordinate systems of all joints are established on the five-axis robot, a six-axis IMU is arranged, and acceleration and angular velocity are periodically collected; taking a plurality of groups of samples in a stationary period, calculating a mean value, offset and covariance, fitting an error ellipsoid, and obtaining a robust model through characteristic decomposition and threshold value clamping; a normalized matrix is constructed to perform unitization and dimension recovery, and die length tolerance checking is set; establishing unit quaternion integral update, and generating an equivalent angular velocity by using gravity consistency to perform primary attitude correction; the chained cascade joint rotation matrix outputs rolling, pitching and yawing, and the attitude and change rate sequence is subjected to statistical segmentation.

The invention relates to the technical field of industrial nondestructive testing, in particular to an automatic welding seam tracking system of a nondestructive testing robot based on multi-modal sensing. Comprising a data acquisition module, a data preprocessing module, a data fusion module, a track generation module and a motion control module. Point cloud data are obtained through a laser scanner, image information is obtained through an RGB-D camera, attitude data are obtained through an inertial measurement unit, after multi-modal data are subjected to denoising, down-sampling and correction processing, data fusion is achieved through Kalman filtering, and continuous weld space trajectory points are output. And the track generation module generates a tail end pose sequence through three-dimensional B-spline fitting and surface normal estimation, and performs pose smoothing processing by adopting quaternion interpolation. The motion control module is combined with differential path keeping and mixed position-force control, it is ensured that the robot accurately tracks along the center line of the welding seam and maintains constant contact force, meanwhile, the process error correction function is achieved, and the stability and reliability of the system in the complex welding seam environment are improved.

The invention relates to a sole glue spraying track generation method based on point cloud data, and belongs to the field of computer vision and industrial automation. The method comprises the following steps: calculating a global absolute phase by adopting an improved three-wavelength phase shift profilometry, and generating a sole three-dimensional point cloud through a monocular coding structured light system; generating a glue spraying track based on a three-coordinate scanning weighted principal component analysis algorithm: segmenting the point cloud along the length direction of the sole to extract an upper edge point set, setting an offset for the upper edge point, performing inward offset to obtain a transition point, and optimizing the transition point for a high-curvature region by adopting polynomial interpolation to obtain a glue spraying track; generating a smooth trajectory by using a third-order non-uniform rational B-spline curve interpolation; and calculating a normal vector of the smooth path through weighted principal component analysis, and finally converting the normal vector into a quaternion pose to control glue spraying operation of the six-degree-of-freedom robot. The shoe sole glue spraying track of the robot can be effectively generated, the path precision is high, the glue line uniformity is good, and the method is suitable for automatic glue spraying of various types of shoe soles.

The invention relates to the technical field of ship control and navigation simulation, in particular to a ship trajectory visualization method and system based on combination of STM32 and Simulink, and the method comprises the following steps: an inertial measurement module and an ultra wide band positioning module collect attitude position information of a ship; the attitude position information is transmitted to an STM32 chip, the STM32 chip fuses the three-axis angular velocity, the three-axis linear acceleration and the three-axis geomagnetic data through a quaternion fusion algorithm to obtain a quaternion calculation result, and the quaternion calculation result and the position information in the three-dimensional space are fused to obtain fused data; the STM32 chip is used for fusing data, the fused data are packaged into data frames, the STM32 chip is used for transmitting the data frames to a ship model in a Simulink platform through a MAVLink protocol, the ship model is used for extracting the attitude angle and space coordinate information of the ship based on the data frames, and the visual motion trail of the ship is generated based on the attitude angle and space coordinate information of the ship. According to the invention, visual display and control algorithm verification are realized, and a semi-physical simulation closed-loop process is completed.

The invention discloses a freight spaceship reentry recovery attitude cooperative control method. The method comprises the following steps: S1, determining key attitude indexes in a freight spaceship reentry recovery process; s2, collecting atmospheric environment parameters in a reentry process, and meanwhile, obtaining earth gravitational field distribution data to provide environment input for a subsequent attitude control algorithm; s3, based on the Newton's second law and the Euler equation, establishing a kinetic model of the freight spaceship, establishing an attitude kinematics model by adopting a quaternion method or an Euler angle method, and establishing an execution mechanism model for a propulsion system of the freight spaceship; through sub-filter local estimation and main filter global weighting, noise and abnormal values are effectively filtered, attitude estimation precision is improved, instruction distribution adopts a pseudo-inverse method in combination with a weighted least square method, an initial instruction is obtained through the pseudo-inverse method, then secondary optimization is performed in combination with execution mechanism constraints, instruction oversaturation is avoided, and attitude estimation accuracy is improved. Extra redundant execution mechanisms are not needed, and the hardware cost is reduced.

The application belongs to the technical field of virtual reality, and particularly relates to a sound-driven VR serious game interaction control method for semi-blind people. The method comprises the following steps: constructing a 3D sound scene space and a motion guide field, and converting a motion path into a sound change rule; collecting and fusing user pose and physiological feedback signals in real time; dynamically adjusting sound source parameters and tactile feedback according to pose deviation to realize non-visual orientation guidance; performing boundary verification and area determination through ray projection to ensure interaction safety; synchronously processing collision by using a bounding box model and quaternion interpolation; and establishing a mapping model of emotional parameters and sound characteristics to synthesize exciting sound effects. Through multi-modal collaborative interaction, the application significantly improves the perception accuracy and safety of semi-blind users in a virtual space, realizes the cooperation of psychological intervention and motion rehabilitation, and has excellent real-time response capability.

This invention addresses the common failure problems encountered by actuators during the flight of heavy-lift launch vehicles in disturbed environments. It studies a method for achieving rocket attitude stabilization and tracking under complex conditions, proposing an adaptive fault-tolerant backstepping attitude control design method. First, to avoid attitude singularities, a quaternion-based kinematic and dynamic model of the launch vehicle oriented towards actuator failures is established. Based on this model, an adaptive fault-tolerant backstepping attitude controller is designed to adaptively estimate and compensate for uncertainties and external disturbances, handling potential failures and ensuring the stability of the control system. The algorithm proposed in this invention is more effective and superior than traditional adaptive backstepping methods.