196 results about "Trajectory control" patented technology

The invention discloses a multi-unmanned aerial vehicle cooperative inspection trajectory control method for optimizing medical area coverage and service efficiency. The method comprises the following steps: firstly, constructing a medical multi-unmanned aerial vehicle auxiliary inspection mobile edge computing system model, defining an unmanned aerial vehicle and mobile user set, and establishing a communication model containing A2G and A2A links, an unmanned aerial vehicle mobile model and an energy consumption model; then taking a joint function of a coverage score, a system throughput and an emergency task completion rate as an optimization target, under energy and communication connectivity constraints, proposing an LT-MADDPG algorithm, adopting a CTDE framework, processing a time sequence state by an actor network integrated with LSTM, fusing global information by a commentator network embedded with Transform through multi-head attention, and finally obtaining an emergency task. And modeling a cooperative relationship between the unmanned aerial vehicles and a medical task priority. Experiments show that the method is superior to a traditional algorithm in the aspects of coverage, service fairness and system throughput, and the medical inspection efficiency and reliability are effectively improved.

The invention provides an intelligent early warning method and system for physiological fatigue of a construction equipment operator, and the method comprises the steps: synchronously collecting the limb movement and equipment trajectory data of the operator through an inertial sensor and a construction equipment control terminal, carrying out the parallel prediction of an operation sequence through a two-channel time convolution network TCN, and recognizing a redundant and fatigue movement mode; dynamic structure features of the construction equipment track are extracted in combination with an improved ConvNeXt network, and the abnormal level of the track is judged based on a graph neural network classifier; a soft behavior-reviewer algorithm is introduced, the physiological fatigue index, the trajectory deviation rate and the trajectory control fluctuation degree are used as state input, and optimization operation suggestions are dynamically output; a diagnosis result is transmitted to an operator in real time through a voice and tactile feedback mechanism, and a sustainable closed-loop regulation and control system is formed. According to the invention, the fatigue identification accuracy and the intelligent intervention capability of the operator in the construction process are obviously improved, and the construction safety and the operation efficiency are improved.

The invention relates to the technical field of underwater robot control, and particularly discloses a visual servo scanning track control system for a deepwater ROV intelligent electromagnetic detection robot, which synchronously acquires visual, acoustic and inertial motion data of an underwater robot and prior geometric data of a to-be-detected structure; visual sparse light spot features and acoustic contour and abnormal region features are extracted, and credibility evaluation is carried out; by establishing a unified state vector and fusing multi-source data and geometric constraints, the accurate pose, the motion state and the perception uncertainty measurement of the robot relative to the underwater structure are estimated in real time; according to the uncertainty measurement, a local expected trajectory used for precise tracking or pose stabilization is generated in a self-adaptive mode; a motion control instruction is calculated through a control law capable of automatically adjusting gain according to uncertainty, and closed-loop control is achieved; according to the invention, continuous, self-adaptive and high-precision execution of detection operation under complex working conditions is realized.

The invention discloses a mechanical arm state limited preset time point control method and system, and relates to the technical field of robot control, and the method comprises the steps: obtaining a joint position tracking error and a joint speed tracking error based on a joint motion state and an expected trajectory of a mechanical arm; based on constrained system states corresponding to the joint position tracking error and the joint speed tracking error, nonlinear transformation is carried out on the joint position tracking error and the joint speed tracking error through an unconstrained state function, and an equivalent unconstrained virtual error variable is constructed in combination with coordinate transformation; based on the unconstrained virtual error variable, designing a sectional sliding mode variable; and designing a second-order reaching law based on the sectional sliding mode variable to form a closed-loop dynamic state, and solving based on the closed-loop dynamic state to obtain a control torque so as to control the mechanical arm joint. According to the method, the trajectory tracking error of the mechanical arm can be converged at the moment specified by a user, the system state is ensured to meet strict physical constraints in the whole process, and the method can be applied to accurate trajectory control in a complex task scene.

The invention relates to the technical field of aircraft control, in particular to a four-rotor unmanned aerial vehicle robust trajectory control method based on a sliding mode control-improved extended state observer. The method comprises the following steps: firstly, establishing a nonlinear dynamic model of the quad-rotor unmanned aerial vehicle, and decomposing the system into a hierarchical control structure of a position outer ring and an attitude inner ring; secondly, a third-order extended state observer (ESO) is designed to estimate composite disturbance of each channel in real time, an improved sliding mode control law with observation error compensation is provided, and the vibration bearing phenomenon is effectively restrained; and finally, proving the asymptotic stability of the closed-loop system through the Lyapunov stability theory. The method is used for solving the problems of low trajectory tracking precision and control signal buffeting caused by underactuation, strong coupling nonlinear characteristics and external disturbance of the four-rotor unmanned aerial vehicle. High-precision trajectory tracking under complex disturbance can be realized, and control signal buffeting can be suppressed at the same time.

The invention relates to the technical field of trajectory control, in particular to a multi-modal motion trajectory real-time tracking method and system for machine control, which performs weight redistribution through channel noise variance and amplitude difference, performs weighted stacking and continuous updating in combination with Kalman filtering, reduces state estimation variance and channel bias transfer, and improves the tracking accuracy of a multi-modal motion trajectory. Time consistency and amplitude consistency of fusion track state data are improved, an overshoot peak value is inhibited, a deviation attenuation process is shortened, abnormal samples in a deviation sequence are rejected through an isolated forest, interference of outliers on parameter updating is reduced, and speed difference and acceleration difference are compared for a correction control sequence, so that the accuracy of correction control is improved. The boundary detection rate and the determinacy of the stage number are improved, the energy change rate is screened through the speed difference and the curvature value of the adjacent stages, the transfer relation is determined, the time sequence coherence and the geometric continuity of stage switching are kept, and the time sequence stability and the stage connection coherence of the control instruction are kept in the multi-degree-of-freedom space.

The invention relates to the technical field of intelligent robot control and artificial intelligence, and discloses a robot dynamic trajectory control method based on reinforcement learning and multi-sensor fusion, and the method comprises the steps: firstly fusing multi-modal sensor data through extended Kalman filtering, and predicting the time sequence distribution of dynamic obstacles through a long-short-term memory network; meanwhile, the dynamic operability and singularity risk factors of the robot are calculated, on this basis, a reinforcement learning network based on an attention mechanism is constructed to generate a high-level motion strategy, the high-level motion strategy is converted into a bottom-layer execution instruction through a variable impedance controller and a convex optimization torque distribution module, and singular points are avoided by using null-space characteristics. In addition, according to the method, model parameters are finely adjusted in real time by monitoring and predicting errors and the safety intervention degree on line. According to the method, organic fusion of logic decision and physical execution is realized through a layered architecture, and the operation safety, robustness and control precision of the robot in a dynamic environment are remarkably improved.

The present application relates to the field of logging while drilling and intelligent steering control technology in oil and gas drilling engineering, in particular to a rotary steering drilling adaptive trajectory control method for image processing. It comprises: receiving original logging while drilling image data frames of downhole transmission channels; generating a high-resolution predicted geological texture map; synchronously generating a feature uncertainty distribution map representing the pixel probability distribution characteristics in the high-resolution predicted geological texture map; calculating the matching degree of the high-resolution predicted geological texture map and a preset stress-induced artifact feature library; generating a sensor contamination marker signal; calling a safety baseline geological model constructed before drilling; calculating the geological texture structure difference value relative to the safety baseline geological model; generating a geological illusion entropy; receiving the geological illusion entropy and the sensor contamination marker signal; and driving a downhole steering tool according to the activated mode. The present application ensures that the steering instruction is always based on the real existing geological information, and avoids the deviation of the drilling trajectory caused by the fictitious texture.

The invention discloses a video generation method and system based on a scene adaptive track and mirror moving control, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, aligning a scene point cloud and a parameterized human motion sequence to a unified coordinate system, generating a ground adaptive three-dimensional track according to a two-dimensional track input by a user and scene geometry, and redirecting the motion; projecting the three-dimensional information into a two-dimensional condition graph based on camera parameters; and finally, in a conditional diffusion model generation process, selectively fusing scene geometric condition information after noise addition into the current denoising latent variable by utilizing a visibility mask generated by the depth map, so as to realize view angle self-adaptive mirror operation control. According to the method, the problems of conflict between character motion and scene, inflexible track control and inconsistent background under camera motion are solved, and a controllable video which is physically consistent and visually real can be generated.

The invention provides a dynamic optimization method and system for a running path of a stacking robot, and relates to the technical field of path optimization, and the method comprises the steps: obtaining a dynamic semantic map carrying a goods allocation priority and a path passing level, and real-time running data containing a real-time load and a multi-task sequence, analyzing to obtain an association relationship among the operation energy consumption, the path steering and the real-time load; further, an improved genetic algorithm is adopted, multi-objective optimization is carried out in combination with goods allocation priorities and path passing levels, and a path optimal solution set is generated; then, taking the path optimal solution set as input, constructing a moving track model by using a time elastic band algorithm, and performing online re-planning in combination with dynamic updating to obtain a reference track and a speed profile; and finally, track control parameters are tracked and generated through the PID controller so as to drive the stacking robot to execute tasks. The operation efficiency, the stability and the execution precision of the stacking robot in the dynamic environment are comprehensively optimized.

The invention discloses a robot fine operation control system based on a diffusion model and 3D vision, particularly relates to the technical field of industrial robot fine operation control, and aims to solve the problem that when an existing industrial robot executes fine operations such as bottle cap screwing and connector plugging in and out in a complex three-dimensional operation scene, the operation efficiency is high. The problems that three-dimensional perception is unstable, the action resolution is fixed, space and contact constraints are difficult to meet, and the task success rate and safety are insufficient are solved. The method comprises the following steps: constructing a voxel feature field updated along with time under a self-body coordinate system, generating interaction complexity and a physical constraint field based on task coding, and linking adaptive resolution and action granularity scheduling, diffusion trajectory generation and closed-loop execution correction to uniformly map environmental geometry, contact conditions and task stages into tail end trajectory control, so as to realize real-time tracking control. Therefore, fine operations such as bottle cap screwing, connector inserting and pulling and flexible object folding can still be stably completed under complex shielding and contact constraint, and the effects of safety and task success rate are both considered.

The invention relates to the field of numerical control machining and computer-aided manufacturing software, and particularly discloses a honeycomb core six-axis ultrasonic cutting track digital control and interpolation optimization system which comprises a track preprocessing module, a parameterized spline interpolation module, a six-axis linkage inverse solution module, a dynamic feed-forward compensation module, an ultrasonic energy adaptive control module and a multi-source information fusion and decision module. By the adoption of the technical scheme, smooth and efficient interpolation of a track, dynamic feed-forward compensation of motion errors and self-adaptive adjustment of cutting energy can be achieved, and therefore the machining precision, the surface quality and the system stability are improved. According to the invention, ultrasonic energy control is originally incorporated into an overall trajectory control closed loop, and real-time accurate regulation and control of cutting energy are realized through the ultrasonic energy adaptive control module. The module is based on force, amplitude, temperature and other multi-sensor information, adopts fuzzy reasoning to intelligently decide output power, and ensures that cutting energy is constant and optimal under different processing conditions.

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 stratum detection and track control in front of a tunnel face of an expressway tunnel, and provides an unfavorable geological exploration method based on inclined drilling. The method aims at solving the problems that in the prior art, a static geologic model is disjointed with an actual stratum, passive response is made to stratum changes, a stratum subdivision adjustment plan is lacked, and a multi-drill-hole collaborative iteration mechanism does not exist. The method comprises the following steps: firstly, collecting multi-source geological data to construct a dynamic geological model capable of being iteratively updated, generating an initial trajectory and presetting a differentiated stratum adjustment plan; integrating sensing, execution and control components to complete calibration and simulation test; then drilling is conducted according to the initial track, and initial calibration, predictive adjustment and real-time response closed-loop control are sequentially executed; and finally, the effect is verified through imaging and rock core analysis, the model is optimized through drilling data inversion, and multi-drill-hole cooperative detection precision iteration is achieved. Stratum detection accuracy and trajectory control precision are improved, and support is provided for safe and efficient development of tunnel construction.

The invention discloses a method and device for achieving track control of a hydraulic mechanical arm, a target operation area is perceived through a depth camera, division of the operation area and marking of obstacles are completed in combination with an artificial intelligence algorithm, and tight coupling of environment modeling and task planning is achieved. According to the method, the plurality of candidate operation areas can be quickly generated and transmitted back to the ground station, an operator only needs to visually select or correct, the manual operation amount is greatly reduced, and the operation efficiency is remarkably improved. According to the embodiment of the invention, the track control precision and the working efficiency of the hydraulic mechanical arm are improved, the dependency degree of operation on artificial experience is reduced, the friendliness of man-machine interaction is enhanced, the safety and the reliability of the system can be ensured in a complex mining area scene, and the method has good application value and popularization prospect.

This invention belongs to the field of robot control, and particularly relates to a mobile robot path optimization system and method supporting track mode switching. The system uses an encoding module to integrate the mobile robot's operating scenario and real-time load status to generate a navigation task seed containing a path cost vector, navigation strategy identifier, and scenario transition point parameters. The central scheduler dynamically selects and calls the corresponding track navigation strategy sub-model or flat ground navigation strategy sub-model based on this navigation task seed. Combined with a spatiotemporal reservation table based on continuous value reservation weights to achieve multi-vehicle conflict avoidance and resource scheduling, executable trajectory control instructions are generated. This invention achieves load-adaptive path planning, efficient multi-vehicle collaboration, and smooth, seamless navigation switching between track and flat ground scenarios ensured by a switching prediction sub-model, significantly improving operational efficiency and system reliability in mixed and complex environments.

The invention discloses a control method of an asymmetric CLLC resonant converter adopting mixed control of track control and PI control. PI control is adopted during steady-state operation of the asymmetric CLLC resonant converter, when input voltage fluctuation is detected, trajectory control is introduced, the frequency of the converter entering the steady state again after the input voltage fluctuation is calculated through the geometrical relationship of a state plane, and steady-state errors are eliminated through a PI controller. The method has the advantages that the combination of the state track control and the linear controller can reduce the voltage change of the output side and shorten the adjustment time when the input voltage fluctuates, so that the problem of severe fluctuation of the output voltage caused by the sudden change of the input voltage when the resonant converter operates is solved, the dynamic performance of the converter is remarkably improved, and the reliability of the converter is improved. Finally, the problems existing in the process of designing the linear controller are solved, the problem of voltage abrupt change of the input side is solved, and the requirements for rapidity and stability are met.

The invention discloses an unmanned aerial vehicle guaranteed performance trajectory control method, and relates to the field of unmanned aerial vehicle tracking control, and the method comprises the following operation steps: S1, carrying out the longitudinal dynamics modeling of a fixed-wing unmanned aerial vehicle; s2, constructing a T-S fuzzy model; s3, a Fuzzy-GCC controller is designed; s4, the LMI of guaranteed performance control is solved; and S5, intelligently optimizing the parameters. According to the unmanned aerial vehicle guaranteed performance trajectory control method, the control models of the fixed-wing unmanned aerial vehicle can be integrated to obtain a unified form, so that the control law design and parameter tuning of the fixed-wing unmanned aerial vehicle are greatly simplified, a complex propulsion system and a longitudinal controller are decoupled through a lookup table, and the control efficiency of the fixed-wing unmanned aerial vehicle is improved. According to the method, the complexity and nonlinearity of the whole model are remarkably reduced while the control precision is kept, a T-S fuzzy model for longitudinal control of the fixed-wing unmanned aerial vehicle is deduced, and a GCC method and a stability criterion thereof are proposed based on the model, so that the global asymptotic stability of the designed nonlinear state feedback controller is ensured.

This application discloses a parking control method, apparatus, device, and storage medium, relating to the field of vehicle control technology, and aims to solve the problem of increased parking difficulty due to reduced parking space. The method includes: responding to a parking command, controlling a vehicle to enter or exit a target parking space along a first trajectory. The first trajectory includes at least a second trajectory and a target arc-shaped trajectory. The second trajectory is a rotational trajectory controlling the vehicle to rotate around its rotation center point, and the target arc-shaped trajectory is a trajectory that satisfies a first collision constraint condition. The first collision constraint condition is used to prevent the vehicle from colliding with either side of the target parking space along its length direction while traveling along the target arc-shaped trajectory. The second trajectory connects with the target arc-shaped trajectory.

The invention relates to the technical field of geological exploration and directional drilling, and discloses a directional drilling system integrating three-dimensional trajectory control and rope continuous coring. The system is composed of a geology and site data acquisition module, a three-dimensional drilling scheme design module, a construction early-stage preparation module, a horizontal directional guide drilling module, a technical casing installation module, a coring operation module, a drilling comprehensive test module and a result acceptance and closed loop module. A complete modular working process including data acquisition, scheme design, construction execution and test acceptance is constructed, and an optimizable technical system is formed from early-stage multi-source data driving design, precise execution and real-time monitoring of all links in construction and comprehensive test and result integration after hole finishing. According to the system, the success rate and the data quality of single exploration can be greatly improved, the system performance can be continuously optimized through experience feedback, and a reliable solution is provided for implementing accurate geological exploration in various limited and complex sites.

The invention relates to a manufacturing method (100) for an electronic trajectory control device for a vehicle. The manufacturing method (100) comprises synchronizing measurements of the rotational speeds of each of the vehicle's wheels at initial times. The manufacturing method (100) comprises determining a first set of trajectory parameters at these initial times. The manufacturing method (100) comprises developing (101) candidate programs for the electronic trajectory control device by machine learning from the first set of parameters and the synchronized measurements of the rotational speeds of each of the wheels. (Fig. 1)

A method may include, at a control system, receiving a trip request specifying an origin boarding zone and a destination boarding zone, determining a path for the trip request, the path extending from the origin boarding zone and along at least a portion of a trunk lane of a roadway system, assigning the trip request to a vehicle, determining, based at least in part on a location of a parking spot where the vehicle is parked at the origin boarding zone, an estimated transit duration of the vehicle from the parking spot to an entrance to the trunk lane, selecting, based at least in part on the estimated transit duration, a moving position-target from a set of candidate moving position-targets, and causing the vehicle to travel from the parking spot to the trunk lane and travel along the portion of the trunk lane by following the selected moving position-target.

The invention relates to the technical field of unconventional oil and gas resource development, in particular to a real-time control method for safe borehole drilling of a horizontal well, which comprises the following steps of: fitting stratum forced vectors of all drilled sections and actual track adjustment vectors of a bottom drilling tool combination to obtain a stratum-drilling tool coupling mapping function; establishing a system error vector for predicting the to-be-drilled section by taking the stratum forced vector of the to-be-drilled section and the actual trajectory adjustment vector of the bottom drilling tool combination of the drilled section as a reference and combining a stratum-drilling tool coupling mapping function; and actual trajectory control parameters of the section to be drilled are determined according to the prediction system error vector, so that the borehole trajectory meets the geological target spot requirement. According to the real-time control method for horizontal section track adjustment, accurate pre-judgment of track adjustment can be achieved, improvement of the precision of horizontal section track adjustment is facilitated, the mechanical drilling speed is increased, and the real-time control method is particularly suitable for well track adjustment in coal seam horizontal section drilling.

An AI-assisted mobile tennis training system integrates a motorized ball delivery device, multi-camera vision, autonomous navigation, and artificial intelligence for adaptive, data-driven player development. The device includes a motorized chassis, ball hopper, programmable ball delivery mechanism with variable spin, speed, and trajectory control, and a navigation subsystem employing omnidirectional wheels and localization based on court line recognition. A multi-camera array captures real-time player and ball movement, while onboard and remote computing modules process the data to determine player position, shot type, and performance metrics. An AI model predicts optimal ball delivery parameters and adapts drills based on player progress. A mobile application provides remote control, drill customization, and performance analytics. The system supports autonomous repositioning, safety monitoring, and individualized training plans, enabling dynamic, responsive, and efficient tennis practice for skill acquisition and improvement in both amateur and professional players.

The invention discloses an unmanned aerial vehicle relay FSO and RF communication method and system based on hybrid reinforcement learning. The method comprises the steps of obtaining a hybrid RF and FSO unmanned aerial vehicle relay joint optimization problem; expressing a hybrid RF and FSO machine-inorganic relay joint optimization problem as a finite time domain Markov decision process; and solving the finite time domain Markov decision process to obtain a joint optimization strategy of the FSO, the RF link switching, the power distribution and the three-dimensional trajectory of the unmanned aerial vehicle. According to the method, an efficient solution is provided for relay secret communication of the unmanned aerial vehicle, deep coupling optimization of discrete link switching and continuous power and trajectory control is realized, and the technical bottleneck that hybrid decisions are difficult to process in a traditional method is broken through.

The invention discloses a robot self-adaptive trajectory control method based on a radial basis function neural network, belongs to the field of intelligent equipment control, remarkably reduces huge calculation burden in a traditional optimization strategy, avoids time and operation space cost caused by the huge calculation burden, does not need to depend on a model training learning process before control, and improves the control precision. Therefore, the trajectory tracking control precision of the continuum robot is effectively improved. The method mainly comprises the following steps: constructing a continuum robot nominal dynamic model through a Lagrange algorithm; designing a radial basis function neural network function for approximation estimation of Coriolis force, gravity, elastic force and external interference in subsequent controller design; subtracting the actual output from the ideal output of the system to form a tracking error z1, and constructing a first error variable; designing a Lyapunov candidate function for the integral of the first error variable, and designing a first virtual controller; the method is mainly used for track control of the continuum robot.

The invention provides a block chain-based vehicle edge computing task unloading method, system and device, and belongs to the crossing field of Internet of Vehicles and edge computing, and the method comprises the steps that a base station extracts data state vectors of a request vehicle, a service vehicle and an unmanned aerial vehicle, and inputs the state vectors into a BDQ network, extracting associated features and calculating three types of Q values of service vehicle unloading, unmanned aerial vehicle unloading and trajectory control; selecting an optimal unloading object for each request vehicle based on the Q value, matching the optimal flight direction and time for the selected unmanned aerial vehicle, and generating a complete decision instruction; after the base station broadcasts the instruction, the service vehicle and the unmanned aerial vehicle receive and process the task; and after the task is completed, the service vehicle packages task-related data into a transaction ciphertext through the adjacent unmanned aerial vehicle, and a non-tampering evidence is formed through the block chain. According to the method, collaborative optimization of task unloading and tracks is realized, credible cooperation of the nodes is guaranteed in combination with the block chain, task delay and node energy consumption are effectively reduced, and the system resource utilization rate and service reliability are improved.

The application discloses a flight path planning method and device, the method comprises the following steps: determining the type of the flight section by the current state information and the task route, so as to determine the end speed of the flight section; determining the current available acceleration according to the current working condition parameter, combining the current state information, the end speed of the flight section and the target horizontal speed to determine the horizontal acceleration instruction and the horizontal expected remaining flight time; taking the horizontal expected remaining flight time as a constraint, combining the current state information, the end speed of the flight section and the current available acceleration to determine the vertical acceleration instruction; and sending the horizontal acceleration instruction and the vertical acceleration instruction to the flight control system to control the flight of the unmanned aerial vehicle. The application aims to solve the problems of the flight precision and task continuity of the existing speed and trajectory control method of the automatic route flight of the unmanned aerial vehicle, and the difficulty in adapting to the complex dynamic environment. The smooth connection of the unmanned aerial vehicle among multiple flight sections is realized, and the flight safety is ensured.

The invention discloses a three-dimensional point cloud reconstruction method and system of a depth camera-encoder, and belongs to the technical field of computer vision and three-dimensional imaging. According to the method, periodic radial deviation characteristics of characteristic point tracks are extracted, the actual rotation center offset is recognized through least square fitting, and a concentricity compensation matrix is constructed to correct camera space coordinates in real time; and then multi-degree-of-freedom attitude sensing is realized in combination with Kalman filtering, a scanning path is optimized through dynamic coordinate system correction and trajectory control, finally point cloud reconstruction is completed based on the corrected pose, and precision is ensured by adopting a geometric verification closed loop. According to the method, the mechanical assembly tolerance is converted into a technical variable which can be compensated on line, the coupling influence of concentricity deviation and attitude error is fundamentally eliminated through error cooperative compensation and an active control mechanism, and the precision and long-term stability of three-dimensional point cloud reconstruction are remarkably improved.