473 results about "Trajectory control" patented technology

Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.

The invention provides an aluminum alloy auxiliary frame welding accurate positioning and quality control method and system. The method comprises the steps that S4, welding gun track control parameters are updated through a dynamic adjustment instruction, laser tracking data are fused to optimize the welding path angle in real time, and the accurate movement track of each section of welding seam is determined; s5, acquiring real-time thermal expansion data during welding execution, monitoring temperature field distribution of a welding seam area by adopting an optical measurement technology, and judging whether thermal deformation adjustment exceeds a preset threshold value or not; s7, micron-sized welding spot positioning operation is executed according to the stable positioning state, the dynamic path optimization result is fused to generate a final welding instruction, and consistent welding seam quality data is output; and S8, after weld joint quality data are obtained, weld joint deviation and geometric consistency are detected through a laser tracking technology, whether welding quality meets design requirements or not is judged, and a quality verification report is generated. According to the method, the precision and consistency of intelligent welding of the aluminum alloy auxiliary frame are remarkably improved.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention relates to the technical field of unmanned aerial vehicle track control, in particular to an unmanned aerial vehicle track control method and system. The method comprises the following steps: firstly, acquiring flight braking parameters and distribution task data of an unmanned aerial vehicle, and extracting low-altitude distribution meteorological condition feature data; then, flight state simulation is carried out based on flight braking parameters and task data, simulation quantification is carried out on flight attitude instability in combination with meteorological conditions, and then trajectory avoidance behavior control learning is carried out; and finally, constructing a trajectory control model by applying a strategy gradient algorithm, and sending the trajectory control model to an unmanned aerial vehicle control center to execute trajectory control. According to the invention, the unmanned aerial vehicle trajectory control technology is optimized, so that the unmanned aerial vehicle trajectory control technology is more perfect.

The invention discloses a video generation system based on a video large model, and belongs to the technical field of video generation. A trajectory control unit; a physical rule constraint unit; the scene generation unit is used for constructing a high-fidelity dynamic environment and enhancing the scene immersion of the video; a timing consistency unit; a multi-modal fusion unit; the user interaction unit is used for realizing man-machine collaborative creation and real-time control and improving the generation efficiency; a quality evaluation unit; the post-processing optimization unit is used for improving the terminal applicability of the output video and adapting to multi-platform requirements; and a distributed deployment unit. Millimeter-level regulation and control of the object motion trail are realized through a multi-dimensional accurate control mechanism, and physical rule constraints are combined to ensure that the generated video conforms to the reality mechanics law, so that the content authenticity is remarkably improved; the multi-modal fusion technology integrates text, voice and image input, accurately restores the intention of the user and realizes lip shape synchronization and emotion migration.

The invention relates to the technical field of equipment collaborative operation of the Internet of Things, in particular to a transit hub equipment collaborative operation method and system based on the Internet of Things technology. According to the method, original attitude motion data of a target ship are collected in real time through an attitude sensor network of the Internet of Things, and a real-time attitude drift vector of the target ship relative to a geodetic coordinate system is calculated; constructing a dynamic coordinate transformation matrix according to the real-time attitude drift vector; acquiring an expected operation track control instruction preset by the quay crane control system according to the geodetic coordinate system; performing coordinate transformation operation on the expected operation track control instruction through a dynamic coordinate transformation matrix to obtain a compensated cooperative control instruction; the compensated cooperative control instruction is transmitted to a quay crane control system in real time through an Internet of Things communication link, and a quay crane lifting appliance is driven by the quay crane control system to conduct lifting operation according to the compensated track instruction; and the safety and efficiency of the hoisting operation between the ship and the quay crane can be effectively improved.

The invention relates to a welding track control method, device and equipment and a storage medium, and the method comprises the following steps: carrying out imaging scanning and welding contour extraction on the surface of a to-be-welded workpiece through a multispectral camera to obtain a welding contour feature sequence; based on the welding contour feature sequence, deep reconstruction is conducted on a welding area of a to-be-welded workpiece, welding three-dimensional point cloud data are obtained, and a welding track space curve is extracted from the welding three-dimensional point cloud data; track segmentation planning is conducted on a preset welding gun on the basis of the welding track space curve, and a segmented motion track sequence is obtained; based on the segmented motion trail sequence, pose mapping is conducted on an end effector of the welding gun, and a joint space motion sequence is obtained; and on the basis of the joint space motion sequence, the welding gun is controlled to conduct welding operation on the surface of the welding workpiece, and the technical problem of precise motion control of the robot welding gun under the complex space posture is solved.

Systems and methods for characterizing a subterranean formation that include a drill bit for drilling a deviated wellbore through the formation and that repeatedly changes direction. Also included is a directional drilling system and measurement equipment to detect a property of the formation. A trajectory control system is operable to: receive a well log; determine a property of the wellbore; segment the well log based on the property; and correlate one of the segments with the well log previous to the segment to determine a matching score for the segment. If the matching score is equal to or greater than a minimum threshold, the segment was taken in the formation and at the same stratigraphical true vertical depth (“STVD”). If the matching score is less than the minimum threshold, the segment was not taken in the formation and not at the same STVD and the STVD is estimated.

The invention discloses a mobile robot path planning method and system based on a circular trajectory control obstacle function, and aims to realize trajectory smooth control in an obstacle avoidance process. Firstly, an envelope circle is used for modeling an obstacle, an actual boundary is approached in a continuous and simple geometric form, and the calculation complexity of collision detection is reduced. Secondly, designing a control obstacle function based on a circular trajectory, constructing a derivable constraint in combination with a geometrical relationship between a tangent line and an obstacle boundary, and achieving trajectory smoothing, reducing speed fluctuation and prolonging the service life of an execution mechanism while guaranteeing a safe distance. For disturbance caused by factors such as ground unevenness, historical data are learned by using a Gaussian process, the disturbance trend is predicted in real time, and the motion stability is improved. And finally, through nonlinear optimization solution, generating control input with safety, continuity and disturbance adaptive capability. According to the method, geometric modeling, safety constraint, disturbance prediction and a dynamic adjustment mechanism are fused, and the safety and adaptability of robot path planning are remarkably enhanced.

The invention provides an automatic driving method and device based on a large language model and a diffusion model. The method comprises the following steps: converting a multi-view image into a multi-view image feature; an environment query vector and a detection query vector are interacted with the multi-view image features, the multi-view image features are compressed and mapped to obtain environment codes, the data volume is reduced, and the calculation efficiency is improved; carrying out interaction on historical image features and the compressed image features, and carrying out coding mapping to obtain historical codes; and inputting the environment code and the historical code into a large language model and carrying out autoregression processing to obtain a track control code. On one hand, historical image features are introduced so that the state of the current vehicle can be judged more accurately, on the other hand, the track control codes are input into the diffusion model to be decoded so that text codes can be converted and correspond to space track positions, the visual language model can be applied to track prediction of automatic driving, and the track prediction accuracy is improved. And the accuracy of trajectory prediction is improved.

The invention provides an automatic cleaning method and system based on plasma and track control, and relates to the technical field of control, and the method comprises the steps: obtaining a three-dimensional model of a workpiece, extracting depth information, dividing the surface of the workpiece into a plane area, a convex area and a concave area based on a principal curvature value, setting differentiated track density parameters for different areas, and setting a track transition zone; and the attitude angle of the robot is calculated in combination with the normal vector, and finally track data containing pose information and plasma cleaning process parameters are generated. According to the cleaning device, precise cleaning of a complex surface is achieved, and the cleaning efficiency and quality are improved.

The invention discloses a drilling trajectory real-time dynamic tracking control method and system based on deep learning, and relates to the technical field of petroleum drilling, and the method comprises the steps: generating well trajectory control parameters according to the basic information of a well and preset well trajectory data; setting an optimal drilling fluid performance parameter corresponding to the current working condition according to the stratum environment where the drilling tool is located; deep learning is conducted on the well track control parameters and the optimal drilling fluid performance parameters through the correlation model, a drilling parameter setting scheme is generated, and an underground drilling tool is controlled to conduct drilling; comparing the drilling data with data in the drilling parameter setting scheme, and determining deviation in the drilling data; and determining a corresponding fault mode, and determining a corresponding solution from the solution library to correct the drilling parameter setting scheme. According to the method, deep learning is carried out on geological conditions and historical data, the well trajectory control model and other models are constructed, the model can be continuously updated and optimized along with data accumulation, and the drilling trajectory tracking and control precision is continuously improved.

The invention relates to a four-way shuttle vehicle based on ontology state perception and a self-calibration positioning method of the four-way shuttle vehicle. The method comprises the following steps: establishing a body state sensing system, collecting three-axis acceleration, three-axis angular velocity, wheel set displacement and attitude angle data of a shuttle vehicle body, and synchronously forming a standardized sensing data set; performing fusion calculation on the standardized sensing data set to obtain real-time position and attitude parameters; calculating an initial value of a positioning error based on matching of the real-time position and attitude parameters with pre-stored high-precision track map data; according to the positioning error initial value, adaptive correction calculation is executed, a real-time positioning error correction amount is determined, and positioning information is corrected; parameters of a shuttle vehicle track control system are updated according to the corrected positioning information, and track navigation control is carried out; accumulated positioning error correction data are periodically analyzed, self-calibration model parameters are optimized, and positioning accuracy and stability are improved. According to the invention, the positioning reliability and path tracking capability of the four-way shuttle vehicle in a complex environment can be improved.

The invention provides a flexible control method and device of a mechanical arm, a storage medium and a computer program product. The flexible control method comprises the following steps: a contact force calculation step: calculating the contact force between the charging plug and the charging socket according to state information by using a contact dynamic model; a track correction step: using an admittance controller to correct the original expected track according to the contact force between the charging plug and the charging socket to obtain a corrected reference track; a track control step: calculating a joint torque according to the corrected reference track by using a mechanical arm dynamic model so as to drive the mechanical arm to track the corrected reference track; and a parameter optimization step: according to the real-time state of the end effector of the mechanical arm and the contact force between the charging plug and the charging socket, the impedance parameter of the admittance controller is optimized through a reward function of a reinforcement learning algorithm. The flexible control method provided by the invention has higher precision, stronger adaptability and continuous optimization capability.

The invention discloses a dynamic trajectory control method for an SCARA (selective compliance assembly robot arm) robot. According to the kinematics model, a homogeneous transformation matrix is used for analyzing the coordinate transformation relation between each joint and the connecting rod, and an initial pose and a target pose are determined; establishing a kinetic model based on a Lagrange equation, and determining kinetic parameters needing to be identified; performing parameter identification by adopting a random weight particle swarm optimization algorithm; according to an identification result, a sliding mode robust item RBF neural network adaptive controller is constructed, real-time track control is achieved, and the end manipulator is made to move to a target pose along a preset dynamic track; a real-time pose is detected through a sensor and compared with a target pose, and arrival is judged when the error is smaller than a threshold value. Model precision and identification efficiency are improved through modeling and efficient parameter identification, the composite controller effectively compensates uncertainty, disturbance and nonlinear factors of the model, closed-loop control and online adjustment are achieved, and trajectory tracking precision, system stability and dynamic response speed are improved.

The embodiment of the invention discloses a tray forking control method and device of an unmanned forklift and a medium, and relates to the technical field of path planning, and the method comprises the steps that multi-mode environment data in a target forking environment is collected in real time through a multi-mode sensor, and based on the multi-mode environment data, region segmentation is conducted on the target forking environment, and the target forking environment is obtained; constructing a dynamic risk map, wherein the dynamic risk map comprises a static obstacle, a dynamic obstacle and a tray target position; according to the dynamic risk map, an improved adaptive sampling search algorithm and a preset node expansion constraint condition are adopted to generate a global forklift path; b spline curve fitting is carried out on the global forklift path, a multi-source error compensation item is introduced into the track control point through a pre-obtained multi-source quantization error, a fitted forklift path is determined, and the target tray is forked through the fitted forklift path.

The invention provides a multi-source information fusion unmanned aerial vehicle marine target tracking method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining image data, and extracting the features of a target region; unmanned aerial vehicle attitude data, radar position detection data and AIS identification data are obtained, attitude disturbance correction is executed, and time axis alignment is performed on the data; calculating the visual feature matching offset of the adjacent frames, and comparing the visual feature matching offset with the radar azimuth change trend; constructing a candidate target spatial position set, and executing distance convergence screening and spatial overlapping constraint processing of a geometric projection range on the candidate target spatial position set; motion direction continuity comparison is executed, and target position data is generated; generating a track control instruction for controlling the flight direction and speed of the unmanned aerial vehicle according to the target position data, and adjusting the subsequent flight path of the unmanned aerial vehicle according to the track control instruction; according to the invention, autonomy and accuracy of unmanned aerial vehicle marine target tracking are improved.

The invention belongs to the technical field of intelligent manufacturing, relates to the directions of numerical control machining, industrial robot trajectory control, complex curved surface path planning and the like, and provides a topology maintenance type curved surface trajectory optimization algorithm fusing surface curvature induction and multi-domain coupling optimization. The method comprises the steps of curved surface preprocessing, initial path construction, multi-domain coupling optimization, topology maintenance, trajectory output and the like. The method comprises the following steps: extracting local curvature characteristics of a free-form surface, adaptively arranging path points, constructing a multi-target optimization model containing indexes such as path uniformity, normal consistency and track fairness, and ensuring that the path direction is consistent with the normal height of the surface through an optimization process. The algorithm adopts an evolutionary optimization strategy to globally adjust the path, and introduces a topology maintenance mechanism to ensure that the original curved surface structure is not damaged in the optimization process. The method is suitable for CNC machining, industrial robot trajectory control and three-dimensional modeling, and has the advantages of being reasonable in path distribution, high in geometric consistency, stable in optimization effect and the like.

The invention provides a pultrusion strip track control automatic gluing method and system based on a servo motor, and relates to the technical field of track control. The method comprises the steps that a coordinate system is established by collecting three-dimensional digital-analog information of a workpiece to plan track points, and a visual sensor is used for recognizing reference feature points for space compensation; the glue amount and pressure parameters are monitored in real time, the speed of the servo motor and the glue supply pressure are dynamically adjusted, and the parameters are uploaded to a control system to generate a quality evaluation report for track optimization. According to the pultrusion strip gluing device, accurate control over the gluing track and the glue amount uniformity are achieved, and the pultrusion strip gluing quality and the production efficiency are improved.

The present disclosure is a device and methods for satellite trajectory control and collision avoidance. Embodiments of the disclosure are comprised of a smart satellite device performing a process three steps. First, sensors collect data about the satellite's physical landing environment, passing information to satellite's database and processors. Second, the processors manipulate the information with a deep reinforcement learning program to produce instructions. Third, the instructions steer the satellite body by manipulating the satellite's panels for optimal trajectory and collision avoidance. The purpose for the present disclosure is to help solve the space debris problem.

The invention relates to the technical field of conveying equipment, in particular to a magnetic suspension flexible conveying system which comprises a stator guide rail module, a rotor suspension module, a suspension gas module, a magnetic force driving module, a control module and a detection module. The stator guide rail module comprises an installation support, a guide rail is fixedly installed on the installation support, the rotor suspension module is installed on the guide rail, air guide holes are evenly distributed in the guide rail, an air guide channel communicated with all the air guide holes is formed in the guide rail, and the air guide channel is connected with the suspension air module. According to the application, gas suspension and magnetic force driving are fused, real-time monitoring is realized through the detection module, precise regulation and control are realized through the control module, high-precision positioning and track control of the rotor suspension module are realized, and the requirements of high-precision industries such as electronics and precise instruments are met. According to the non-contact design, friction and part abrasion are greatly reduced, the operation resistance is small, the energy consumption is low, the system stability is high, interference and load change can be rapidly adjusted, and the reliability is high.

The invention relates to the technical field of turning machining, and discloses an ultra-precision turning machining control method and system and a storage medium. The method comprises the steps that a laser contourgraph detects geometric parameters and abrasion data of a turning tool, and a tool health degree index is calculated; performing error compensation based on the health degree index to obtain a feeding control parameter; cutting force signals are collected, and technological parameters are optimized through a multi-target genetic algorithm; arranging a temperature sensor array, and adjusting cooling control according to process parameters; and generating a track control point and performing interpolation processing to obtain a motion control sequence. According to the method, the problem that the machining precision is unstable due to the fact that the cutter state cannot be quantitatively evaluated in real time in traditional turning is solved, and the precision control capability and machining quality consistency of ultra-precision turning are improved.

The invention discloses a directional drilling track self-adjusting measuring system. Relates to the technical field of directional drilling. The method is used for solving the problem that the current coal mine directional drilling track control technology cannot meet the requirements of underground safety production for high precision, fast response and strong adaptation. According to the technical scheme, the system comprises a multi-dimensional sensor module, a data processing module, a control module, an execution mechanism and a feedback module. According to the technical scheme, the directional drilling track self-adjusting measuring system is provided, the system response speed and track adaptability are remarkably improved, and the track qualification rate is increased to 98% or above from 70%-80%.

The invention relates to the technical field of coffee latte robots, in particular to a robot task processing method and system for coffee latte. The method comprises the following steps: obtaining a target garland pattern instruction, and carrying out feature extraction on an input instruction through an image recognition and semantic analysis model; constructing a cup body coordinate system, and performing adaptive modeling on different apertures, depths and cup-shaped structures based on the cup body coordinate system and the collected features to generate a cup body space attitude parameter set; generating a time sequence track set of the robot end effector based on the corrected time sequence stable track set in combination with the state vector; and matching with a comparison target structured representation matrix, optimizing a trajectory control parameter and a fluid jet parameter, and realizing real-time adaptive adjustment and stable output of a garland result. By dynamically adjusting the noise covariance matrix, track deviation caused by liquid level disturbance and cup body shaking is corrected in real time, and the problems of garland stroke breakage and deviation caused by external disturbance are solved.

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 robot control, in particular to a complex distribution network operation environment-oriented mechanical arm track obstacle avoidance control method, which comprises the following steps of: acquiring geometric information of an operation environment, a mechanical arm body posture and a grabbed target object through a multi-modal sensor; performing time synchronization and coordinate system calibration on the information to obtain environment data under a unified reference system; on the basis of the environment data, discretizing a work space into a three-dimensional voxel grid, calculating the occupancy probability of each voxel, and generating a probability occupancy model in combination with dynamic obstacle recognition and motion prediction; according to the method, through multi-modal sensing and unified reference system modeling, the problem that dynamic obstacle avoidance fails due to the fact that most of traditional mechanical arm track control methods adopt static environment hypothesis and cannot sense dynamic environment changes in real time and cannot unify multi-source information reference is solved.

The invention provides an automatic robot and a method. The method comprises the steps of generating an initial motion track of a target robot through task execution environment information; calculating the trajectory deviation value of the target robot in the operation process based on the pose data collected in real time and the initial motion trajectory; performing segmented extraction on the trajectory deviation value to obtain a trajectory deviation sequence of the target robot, predicting a predicted trajectory error of the target robot in a dynamic environment through a trajectory error prediction model and the trajectory deviation sequence, and constructing an error correction factor of the motion trajectory of the target robot according to the predicted trajectory error; obtaining fusion correction track characteristics of the target robot in a dynamic environment according to the motion track corrected by the error correction factor; and driving the target robot to perform adaptive trajectory control in a dynamic environment based on the fused corrected trajectory features. By adopting the scheme, the adaptive trajectory control capability of the automatic robot can be improved based on accurate prediction of the trajectory deviation of the automatic robot in a dynamic environment.

The invention relates to the technical field of automobiles, in particular to a four-wheel steering vehicle track control method based on model predictive control. Firstly, a rear wheel proportionality coefficient changing along with the longitudinal speed of the vehicle is constructed; expressing a rear wheel steering angle as a function of a front wheel steering angle based on the proportionality coefficient, and substituting the function into a traditional three-degree-of-freedom four-wheel steering model to construct a simplified three-degree-of-freedom dynamic model with lower control dimension; performing linearization and discretization processing on the simplified model, and constructing an MPC prediction model; constructing a control objective function and related constraint conditions; and performing rolling optimization solution on the control target function to obtain an output rotation angle control instruction. According to the method, the complexity of a control problem is effectively reduced through model simplification, and the real-time performance and stability of a control system can be remarkably improved while the vehicle trajectory tracking precision is ensured.

The invention discloses a multi-axis servo time sequence control system based on a real-time controller, which belongs to the technical field of motor control and realizes the effects of high precision, complex track and expandability through real-time interaction of a real-time controller module, a servo driver module, a sensor module, an upper computer, a multi-axis time sequence control module and a control panel. High-precision track control is carried out through the track control unit, the requirements for accuracy and smoothness in the machining and moving process are met, coordination and synchronization of the moving states of all the axis servo motors are achieved through the time sequence planning unit and the synchronous control unit, collision and interference among all the axis motors are avoided, and the machining precision is improved. And the system stability and reliability are enhanced.