51 results about "Kinematic modeling" patented technology

The invention discloses a forward and reverse design and simulation method for virtual excavation of a sunken vertical shaft heading machine. The method comprises the following steps: S1, initializing a terrain; s2, kinematic modeling of the heading machine; s3, track surface generation and virtual excavation; s4, forward design; s5, carrying out reverse design; and S6, outputting data. According to the forward and reverse design and simulation method for virtual excavation of the sinking type vertical shaft heading machine, geometric-level virtual excavation simulation can be realized based on geometric parameters, a kinematic model and stratum data of a VSM; the excavation effect of the preset excavation parameters can be verified through forward design, and a feasible excavation parameter set can be intelligently and reversely solved from the target excavation morphology through reverse design; and meanwhile, a standardized geometric model and structured data which can be directly used for subsequent engineering numerical analysis and construction guidance can be exported, so that the parameter determination precision, the process controllability and the scheme optimization capability before construction are improved.

The invention relates to the technical field of automatic guided vehicle control, in particular to a multi-differential wheel set AGV trajectory tracking method and system, and the method comprises the steps: building a special kinematics forward / inverse solution model, and determining the speed and angle mapping relation between a driving wheel and the center of a vehicle body; constructing a double-layer framework of upper-layer pure tracking trajectory tracking and lower-layer wheel set cooperative control, and introducing a steering cooperative optimization strategy; an intelligent self-correction mechanism is set, and a strong correction mode or a smooth correction mode is selected according to the deviation distance; and deep fusion of a pure tracking algorithm and a special chassis is realized, and a complete closed-loop control system is formed. The system comprises a chassis module, a kinematics modeling module, a double-layer control module, a self-correction module and a closed-loop feedback module. According to the method, the problems of poor adaptability, motion coupling, unbalanced heavy AGV tracking precision and stability and the like of a traditional algorithm are solved, high-precision trajectory tracking is realized, tire wear and energy loss are reduced, the robustness of the AGV in a complex environment is enhanced, and the method is suitable for heavy and heavy-load AGVs.

The invention discloses an IMU (Inertial Measurement Unit) drift error calibration method for long-term attitude measurement of an industrial robot. The IMU drift error calibration method comprises the following steps: deducing a pose matrix between a robot base coordinate system and a navigation coordinate system; deriving a pose matrix between a robot tail end coordinate system and a sensor body coordinate system; kinematics modeling is conducted on the industrial robot, and motion parameters are obtained; deriving a homogeneous transformation matrix between two adjacent joints according to the kinematic parameters; acquiring actual joint corner information when the robot runs and actual information measured by a tail end IMU when the robot runs; wherein the actual information of the IMU comprises actual angular velocity information and actual attitude information; the collected actual joint rotation angle information is substituted into the homogeneous transformation matrix, theoretical attitude information of the tail end of the robot is obtained, and the theoretical attitude information is converted into a navigation coordinate system; converting the theoretical attitude information converted into the navigation coordinate system into an Euler angle; designing a Kalman filter; according to a Kalman filter, data fusion of theoretical attitude information and actual attitude information is completed, and calibration is achieved. According to the method, joint angle information, the industrial robot end theoretical attitude obtained through forward kinematics calculation of the kinematics model and actual attitude data obtained through IMU measurement are fused, and accumulated error calibration formed in long-term attitude measurement of the IMU is achieved.

The invention discloses a joint calibration method based on a rotary laser radar and a camera, and the method comprises the following steps: S1, carrying out the kinematic modeling of the camera, the laser radar and a motor rotating shaft based on a DH parameter method; s2, collecting synchronous laser radar point cloud data and camera image data of a motor rotating shaft at different angles; s3, processing the laser radar point cloud data; s4, processing the camera image data; s5, projecting the extracted laser point cloud depth continuous edge features to an image plane based on a current external parameter estimation value, and matching and associating the extracted laser point cloud depth continuous edge features with the image edge features extracted in the step S4; s6, constructing a projection residual equation about the external parameters according to the successfully matched feature pairs; and S7, the residual equation is linearized. According to the joint calibration method based on the rotary laser radar and the camera, high-precision and high-robustness automatic calibration is completed in an environment without an auxiliary target through kinematics modeling of a system and an advanced feature extraction matching technology. The invention further discloses a joint calibration device and system based on the rotary laser radar and the camera, and a storage medium.

The invention relates to a human body multi-cycle gait track prediction method based on small sample space-time decoupling. The method comprises the following steps: firstly, obtaining a single-cycle gait sample through kinematics modeling and cycle segmentation, and extracting, expanding and normalizing parameters such as stride and stride frequency; secondly, establishing a gait parameter joint distribution model by using a Gaussian mixture model and Gaussian mixture regression, and predicting gait parameters according to the target speed; and then space and time decoupling reconstruction is carried out, a high-fidelity joint trajectory is generated by adopting an adaptive collaborative attention LSTM network in space, a zoom factor is calculated based on a predicted stride frequency and a target speed in time, a time sequence is adaptively adjusted through a dynamic motion primitive, and finally a multi-period and multi-speed high-fidelity gait trajectory is generated through fusion. The method has high precision, interpretability and strong generalization ability under the small sample condition, the generated gaits are natural and coordinated and accord with the biomechanical law, and the application cost of a humanoid movement device is remarkably reduced.

The invention discloses an inverse kinematics method for a redundant seven-degree-of-freedom robot. The method specifically comprises the following steps: step 1, establishing forward kinematics modeling of the seven-degree-of-freedom robot; step 2, inputting a target pose, and defining a tail end pose error of the seven-degree-of-freedom robot as a Riemannian distance on a three-dimensional special Euclidean group manifold; 3, adopting a three-stage hybrid optimization strategy; 4, carrying out constraint processing between joints; and 5, verifying convergence and robustness. The adaptive differential evolution algorithm is used as a core optimizer, parameters of the adaptive differential evolution algorithm can be dynamically adjusted along with an iteration process, and global exploration and local development capabilities are balanced; and a dynamic parameter adjustment strategy of nonlinear progressive increase of a scaling factor and linear progressive decrease of a crossover probability is designed, and a DE / current-to-pbest variation strategy is adopted, so that the algorithm pays attention to global exploration in the initial stage of iteration and is biased to fine development in the later stage, and the convergence speed and the global search capability are effectively improved.

The invention discloses a continuum robot position tracking and joint limiting avoidance method, and belongs to the field of robot control. According to the method, a current path is obtained through forward kinematics modeling, joint limiting constraint and ideal path generation are combined, a nonlinear programming problem with the maximum path deviation and the minimum path deviation as the target is constructed, and joint increment is iteratively solved to achieve end tracking and overall path fitting. According to the method, the problem that inverse kinematics is difficult to solve is avoided, joint overrun is explicitly avoided, and the method is suitable for high-precision and high-reliability control in a narrow space and an obstacle environment.

Disclosed are a rigid-flexible coupling continuum robot unit and a continuum robot. In the rigid-flexible coupling continuum robot unit, structural unit blocks are connected to each other in an interlaced manner to form a whole mechanical arm; a flexible nickel-titanium rod is connected to two adjacent structural unit blocks at two ends, respectively; a front-end unit block is arranged at the frontmost end of the mechanical arm; and a rear-end unit block is arranged at the rearmost end of the mechanical arm. In use, the memory alloy nickel-titanium rod forms a flexible joint, a limiting clamping jaw and a limiting boss form a rigid joint, the two joints complement each other, after a driving wire exerts a pressure, the limiting clamping jaw and the limiting boss are in a wrapping relationship, the movement track of the robot unit is limited, the robot unit always rotates along a circular arc around a rotation center, and the robot unit has the advantages of simple and accurate kinematic modeling, strong torsion resistance, strong load bearing capacity and the like, and is suitable for operation in various complex environments.

The invention relates to the technical field of intelligent manufacturing, and discloses an enterprise production data traceability control method based on artificial intelligence, and the method comprises the steps: actively implanting a digital coding disturbance sequence as a physical traceability fingerprint, and combining with kinematics modeling based on a Lagrange view angle, thereby achieving the traceability control of enterprise production data. Compared with a traditional statistical matching method which only depends on waveform geometric similarity, the method has the advantages that a physical sensing time sequence alignment mechanism is constructed, a time mapping path is forcibly required to follow the displacement conservation and entropy increase law, and the accuracy of the real-time tracking is improved. According to the technical scheme, mass balance verification is further utilized to carry out physical checking calculation on a traceability result, and the technical span from signal correlation advanced to physical causality effectively eliminates false alignment and quantization errors caused by shutdown and speed change, ensures absolute accuracy and non-tampering property of responsibility entity locking, and improves the reliability of the system. And the robustness and legal credibility of the industrial data management and control system are greatly improved.

The invention discloses a method for analyzing the influence of a rod length error and a kinematic pair gap on the motion of an ornithopter. The method comprises the following steps: carrying out kinematic modeling on an ornithopter structure to obtain a kinematic model; acquiring a motion trail of the end point of the tail end of the outer wing in the kinematic model as a first influence factor; the average flapping position change of the rod piece at the tail end of the outer wing is obtained in the kinematic model to serve as a second influence factor; performing margin modeling on the ornithopter structure in combination with the first influence factor and the second influence factor to obtain a margin model; and performing reliability solution analysis on the input ornithopter to be analyzed according to the margin model. According to the method, the error influence can be accurately captured, and the actual working state of the ornithopter can be more accurately reflected by defining the motion trail of the end point of the tail end of the outer wing and the average flapping position change of the rod piece at the tail end of the outer wing as influence factors of specific reliability. The two indexes are directly associated with the key parameters of the flight performance of the ornithopter, thereby ensuring that the evaluation of the system reliability is closer to the reality.

The invention discloses an endoscope active following motion control method, which comprises the following steps of: 1, carrying out kinematics modeling on a continuum endoscope, obtaining a pose transformation matrix of the continuum endoscope so as to obtain a working space of the continuum endoscope, and verifying a reachable range of the working space in an eyeball; step 2, constructing a visual servo following controller based on the endoscope image, collecting the endoscope image and identifying the tip of the surgical instrument in the endoscope image based on an image processing algorithm of a YOLO neural network architecture; and extracting the deviation between the pixel space coordinates of the feature points and the expected features in real time, and controlling the camera at the tail end of the continuum endoscope to move based on the deviation of the feature points until the image features reach a target state. The multi-view intraoperative image can be provided in the continuous bending movement process of the continuum endoscope, the tail end of a surgical instrument is followed in real time in the operation, the posture of the surgical instrument in the operation is observed in real time, and the automation degree of the continuum endoscope is improved.

The invention relates to the technical field of automation equipment control, in particular to a multi-dimensional configuration description and modeling method for an industrial robot, and solves the problems that only kinematics modeling can be performed and peripherals, communication and machining programs cannot be covered in the prior art. The method comprises the following steps of S1, dividing independent functional parts of an industrial robot machine into peripheral parts, communication parts and basic parts according to function uniqueness; s2, an object-oriented modeling method is used for modeling the three types of components from the dimensions of motion ability, function interface and interaction logic; s3, modeling results are integrated, the basic part is used as a carrier, peripheral and communication part modeling data and configuration parameters are matched, a machining program is designed in a flow chart compiling mode, and a configuration description file set is generated; and S4, loading the file set to a development platform program, controlling industrial robots with different configurations, and supporting construction of digital twin objects. According to the method, a multi-dimensional modeling system is constructed, a physical structure and motion logic are covered, key production links are incorporated, and model description and whole-process control are realized.

The invention discloses a mechanical arm kinematics modeling and motion control method, device and system. The mechanical arm comprises N joints connected in series and a sand disc connected to the tail end, and a radial distance R exists between the working edge of the sand disc and the tail end arm section. The kinematics equation construction method comprises the following steps: determining a working edge reference point of a sand disc as a motion control target; based on mechanical structure parameters, joint variables and a radial distance R of the mechanical arm, forward and inverse solution equations of the kinematics position, speed and acceleration of a working edge reference point are deduced. The motion control method comprises the following steps: acquiring a discrete expected position, speed and acceleration point; calculating an expected joint angle, angular velocity and angular acceleration by using a position inverse solution equation, a velocity inverse solution equation and an acceleration inverse solution equation in sequence; and giving a calculation result to a multi-axis motion controller in real time. According to the method, the radial distance of the sand disc is incorporated into the kinematics model, feedforward control of three levels of position, speed and acceleration is achieved, and the motion control precision of the non-standard mechanical arm is remarkably improved.

This invention belongs to the field of intelligent manufacturing technology and discloses a real-time data-driven intelligent digital twin system for the docking of aerospace components. First, a standardized communication interface is established between multi-source sensors and various modules of the system to collect and transmit sensor data in real time. Second, based on the real-time multi-axis displacement data of the positioner, the real-time motion trajectory of the component is derived through kinematic modeling, completing the spatial pose parameter calculation of the component. Finally, the component pose parameters and multi-dimensional force sensing data are fused to construct a dynamic and interactive three-dimensional digital twin interface, realizing twin monitoring of the component's motion state and internal forces during the docking process. This system, based on the Unity3D engine, achieves real-time synchronous mapping of multi-dimensional force information and motion attitude parameters. Through a closed-loop processing flow of data acquisition, analysis, and visualization, it realizes quality assessment and quantitative analysis of the aerospace component assembly process, significantly improving the controllability of the docking process.

This invention discloses a method for inverse kinematics modeling of a polyhedral mobile robot, comprising: S1, constructing a forward kinematics model of a polyhedral open-chain robot, defining a kinematic space with the edge lengths of a virtual polyhedron as the core, and establishing a mapping relationship between joint angles and the edge lengths of the virtual polyhedron; S2, constructing an optimization objective function based on the Hadamard product based on the error between the target edge length set and the actual edge length set of the virtual polyhedron; S3, generating an initial set of iteration points covering all quasi-convex regions in the joint space of the polyhedral open-chain robot; S4, solving the gradient of the objective function and iteratively updating the joint angles; S5, setting a convergence threshold and termination condition, performing convergence judgment on the iteration process, and selecting the optimal solution for inverse kinematics; This invention achieves accurate modeling of the inverse kinematics of a tetrahedral mobile robot by constructing an error optimization model based on the edge lengths of a virtual polyhedron and combining Monte Carlo sampling initialization and gradient descent iterative solution.

This application relates to a method, apparatus, and surgical robot for generating control information for a robotic arm. The method includes: determining a target operating position; determining motion control information for the robotic arm based on the target operating position through kinematic modeling; inputting the motion control information into a pre-trained neural network model to obtain a predicted operating position corresponding to the motion control information; if, based on the target operating position and the predicted operating position, the predicted operating position does not meet the requirements, adjusting the motion control information and inputting the adjusted motion control information into the neural network model to obtain a predicted operating position corresponding to the motion control information; until, based on the target operating position and the predicted operating position, the predicted operating position meets the requirements, the final adjusted motion control information is used as the control information for the robotic arm. This method can improve the control accuracy of the robotic arm.

The application provides a sand suction and filling multi-arm cooperative control algorithm based on kinematics and dynamics coupling, comprising the following steps: S1: water dynamic disturbance field full working condition data acquisition and feature library construction; S2: multi-arm kinematics modeling and unified mapping of working space; S3: construction of a multi-arm kinematics-dynamics coupling model embedded with a water dynamic disturbance adaptive compensation term; S4: multi-arm task-dynamics coupling distribution based on an improved parasite-prey algorithm; S5: trajectory-task bidirectional coupling planning based on a fractional order pseudospectral method; S6: multi-arm-filling cooperative closed loop control based on a port-controlled Hamilton fractional order passive control; and S7: real-time acquisition of sand suction and filling parameters and full system closed loop feedback. Through deep coupling modeling of kinematics and dynamics and bidirectional cooperative optimization of core control algorithms, the application realizes high-precision, high-stability and high-efficiency cooperative control of multi-arm sand suction and filling operation in a dynamic water environment.

The invention discloses an automatic measurement simulation platform for large components. The automatic measurement simulation platform comprises an equipment modeling module, a simulation scene loading module, a feature extraction module, a measurement planning module and a measurement execution module, the equipment modeling module is used for carrying out kinematics modeling on measuring equipment and carrying equipment of large components and constructing a measurement constraint model and a combined measurement constraint model of each measuring equipment; the simulation scene loading module is used for constructing a large component automatic measurement scene, loading equipment and a large component model; the feature extraction module is used for selecting a to-be-measured appearance feature surface through interactive feature recognition and performing feature extraction on the selected feature surface; the measurement planning module is used for performing scanning path planning and station planning; and the measurement execution module is used for driving the equipment model to move in the simulation scene according to the planning result, dynamically demonstrating the simulation process and outputting a measurement result. According to the invention, a large component measurement scene and equipment movement can be accurately simulated, and feature extraction and measurement planning are automatically completed.

The application provides a kinematics-based redundant robot obstacle avoidance trajectory tracking method, which comprises the following steps: modeling a target redundant robot through forward kinematics to obtain a forward kinematics equation; modeling an obstacle as a sphere, modeling each link of the target redundant robot and adjacent joints as capsule bodies, and then calculating the minimum distance between the obstacle and each link of the target redundant robot according to the relative position relationship between the obstacle and each capsule body, and establishing an obstacle avoidance penalty function based on the minimum distance; converting the obstacle avoidance trajectory tracking problem of the target redundant robot into a constraint optimization problem, establishing a target function and constraint conditions according to an expected trajectory, the forward kinematics equation and the obstacle avoidance penalty function; searching for an optimal solution of the target function under the constraint conditions by using an improved artificial bee colony algorithm, and taking a group of joint trajectories corresponding to the optimal solution as the obstacle avoidance trajectory of the target redundant robot; and enabling the target redundant robot to realize obstacle avoidance in the process of executing a tracking task.

The present application relates to the technical field of humanoid robots, and discloses a redundant robot arm inverse kinematics analysis method considering joint limiting and obstacle avoidance, comprising the following steps: kinematics modeling of the redundant robot arm, definition of arm angle parameters and derivation of the analytical inverse kinematics relationship of each joint angle with respect to the arm angle; mapping analysis of joint limiting and singularity constraint based on the analytical inverse kinematics relationship of each joint angle with respect to the arm angle, to obtain the first feasible range of the arm angle; sampling of the arm angle combined with collision detection, to obtain the second feasible range of the arm angle satisfying the obstacle avoidance constraint; intersection operation of the first feasible range and the second feasible range, to obtain the comprehensive arm angle feasible region and select the target arm angle therefrom; and substitution of the target arm angle into the analytical inverse kinematics relationship, to obtain the angle of each joint of the redundant robot arm.

The invention provides a robot FKM kinematics modeling method, and aims to solve the problems of rotation and translation coupling, non-intuitive parameter significance, complex modeling and the like in the existing robot kinematics modeling process. The method comprises the following steps: establishing a joint coordinate system of the robot, and determining a transformation parameter of each joint and a homogeneous transformation matrix of an adjacent coordinate system; wherein the expression of the homogeneous transformation matrix has two forms according to different joint types, and the expression of a rotating joint is as shown in the specification, and the expression of a moving joint is as shown in the specification. A translation transformation matrix of the coordinate system relative to the coordinate system is in the formula; the rotation transformation matrix is a rotation transformation matrix rotating around the translated X axis; the forward kinematics model of the robot can be obtained by sequentially multiplying the homogeneous transformation matrixes of all the joints for the rotating joints as a rotation transformation matrix rotating around the rotated Z axis and for the moving joints as a translation matrix moving along the rotated Z axis. According to the method, basic transformation and joint variables of the coordinate system are decoupled, so that the modeling process is more visual, the parameter significance is clearer, and the method can be widely applied to the field of kinematics modeling and control of industrial robots and service robots.

The invention provides a combined multi-modal attention-dependent knee joint adduction moment estimation method and system, and the method comprises the steps: S1, building a deep learning model architecture through a long-short-term memory encoder, introducing a self-difficulty perception loss function into a model, and carrying out the use in a training process; s2, enabling the deep learning model to carry out multi-modal data fusion, and fusing data from the wireless inertial measurement unit and the wireless myoelectricity measurement unit of multiple features; s3, after the deep learning model fuses the features, on the basis of kinematics modeling, the estimation of the knee joint adduction moment is calculated by mapping the multiple features to the force arm and the ground counter-acting force. According to the invention, the efficiency and accuracy of knee joint health management are improved, and greater autonomy and sense of control are brought to patients.

This invention discloses a method for generating and controlling robot coverage paths based on magnetic adsorption constraints, applicable to rust removal and painting operations on ship hull surfaces. By constructing a differential-speed mobile chassis based on strong magnetic adsorption, stable attachment and movement of the robot on metal surfaces are achieved. Based on kinematic modeling, decoupled control of the robot in different coordinate systems is realized. A coverage path generation algorithm is employed to achieve efficient full coverage of the work area; a multi-source information fusion state estimation method is established to achieve real-time acquisition of the robot's pose. The proposed method exhibits good adhesion stability, trajectory tracking accuracy, and operational continuity under complex curved surfaces and interference environments, effectively improving the automation level and efficiency of ship hull surface treatment operations.

The invention provides a kinematics and dynamics coupling-based sand suction and filling multi-arm cooperative control algorithm. The algorithm comprises the following steps of S1, collecting all-condition data of a hydrodynamic disturbance field and constructing a feature library; s2, multi-linkage-arm kinematics modeling and operation space unified mapping are carried out; s3, constructing a multi-linkage-arm kinematics-dynamics coupling model embedded with a hydrodynamic interference self-adaptive compensation item; s4, multi-linkage-arm task-dynamics coupling distribution based on an improved parasitism-predation algorithm; s5, trajectory-task bidirectional coupling planning based on a fractional order pseudo-spectral method; s6, multi-arm-filling cooperative closed-loop control based on port controlled Hamiltonian fractional order passive control is carried out; and S7, sand suction and filling parameters are collected in real time, and closed-loop feedback is conducted on the whole system. Through deep coupling modeling of kinematics and dynamics and two-way collaborative optimization of a core control algorithm, high-precision, high-stability and high-efficiency collaborative control of multi-linkage-arm sand suction filling operation in a dynamic water area environment is achieved.

This invention discloses an automatic target positioning and alignment system and method for high-altitude rescue based on visual topological association. Building images are acquired through visible light and infrared sensors integrated at the end effector, and a dual-branch deep learning model is used to identify balcony arrays and trapped personnel in parallel. Geometric features of the balconies are extracted to construct a two-dimensional topological matrix, and a fusion association algorithm based on overlap weight and center distance compensation is used to automatically determine the balcony number and spatial location of the trapped person. Intrinsic parameters, laser ranging, and boom attitude data are fused, and pixel coordinates are converted into three-dimensional coordinates of the rescue vehicle base through kinematic modeling. During the approach process, visual servo closed-loop control is switched to correct the motion vector in real time, achieving flexible and precise alignment between the platform and the balcony edge, with an alignment accuracy of ≤±5cm. This invention significantly improves the accuracy of target positioning and the safety of automated docking in complex rescue environments.

The application discloses a kind of six-axis industrial robot positioning error compensation methods, comprising: robot is kinematically modeled and joint space is divided, and the first three-axis joint space and the last three-axis joint space are respectively grid discrete, grid point is as sampling point;Laser tracker is switched to robot base coordinate system;The sampling point of the first three-axis joint space is tracked measurement, utilizes special tool to simultaneously collect three point position information at the same sampling point to calculate the position and attitude error corresponding to sampling point;Laser tracker is switched to robot wrist coordinate system;The sampling point of the last three-axis joint space is tracked measurement, calculates the positioning error corresponding to sampling point;According to the pose error data of sampling point, the pose error of target point is calculated;According to the pose error obtained, positioning error compensation is realized.The application can effectively solve the dimension disaster problem existing in the joint space grid compensation of six-axis industrial robot, and improve the positioning accuracy of robot.

The application discloses a kind of multi-axis industrial robot automatic assembly operation systems, it is related to industrial robot technical field, and the system integrates multi-source environment perception and workpiece positioning module, complete to be assembled workpiece pose solution and assembly reference coordinate output;Smooth continuous assembly motion trajectory is generated through kinematics modeling and trajectory planning module, and the collaborative decoupling control of position loop and force loop is completed in combination with force-position hybrid control module;Built-in assembly action sequence intelligent decision, end effector adaptive regulation and control, assembly process online error compensation module, while configuring whole-process safety protection, data closed-loop management and central collaborative control module, realize the automatic assembly operation of multi-axis industrial robot.The application improves the precision and stability of robot assembly, enhances the system scene flexible adaptation ability, perfects the whole-process closed-loop control and safety protection of assembly, and provides reliable technical support for industrial automation assembly.

The application relates to the technical field of intelligent manufacturing, and discloses an enterprise production data traceability management and control method based on artificial intelligence. The present application implants a digital coding disturbance sequence as a physical tracking fingerprint, combines kinematics modeling based on the Lagrange perspective, and solves the technical pain point that precise tracing cannot be achieved in a non-steady production environment. Unlike traditional statistical matching methods that only rely on waveform geometric similarity, the present application constructs a physical perception time sequence alignment mechanism, and forcibly requires the time mapping path to follow the displacement conservation and entropy increase law. Furthermore, the present application uses mass balance verification to physically check the tracing results. This technical leap from signal correlation to physical causality effectively eliminates false alignment and quantization errors caused by shutdown and speed changes, ensures the absolute accuracy and non-tamperability of the responsibility entity lock, and greatly improves the robustness and legal credibility of the industrial data management and control system.

The invention relates to the field of robots, and provides a six-axis robot control method and device, electronic equipment and a storage medium. A first compensation angle and a second compensation angle which are used for compensating axis perpendicularity errors of a fourth joint and a fifth joint and axis perpendicularity errors of the fifth joint and a sixth joint are introduced into DH parameters; the kinematic model can accurately reflect the actual geometric structure of the non-vertical spherical wrist, so that the modeling deviation caused by neglecting the assembly error in the traditional method is overcome, on the basis, inverse kinematic solution is performed based on the DH parameter containing the compensation angle in combination with the expected pose matrix and the shaft configuration parameter input by the user, and the calculation accuracy is improved. According to the method, the expected pose is accurately converted into the joint angles of the six joints, the robot is controlled to move according to the joint angles, the tail end positioning precision is remarkably improved, and therefore the actual control precision of the robot can be greatly improved only through kinematics modeling optimization on the premise that the mechanical structure is not changed.