338 results about "Trajectory control" patented technology

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 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 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 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 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 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 discloses an electro-hydraulic control underground adjustable angle device and an adjusting method thereof. A translation assembly and a rotating assembly in a shell assembly are sequentially arranged from outside to inside, a sliding sleeve of the translation assembly slides in the axial direction of a shell of the shell assembly, and a spiral rod of the rotating assembly is matched with the sliding sleeve through a spiral key to achieve rotating movement and axial movement of the spiral rod; the lower end of the rotating assembly is meshed with the rotating joint assembly through a spherical gear to realize rotation of a rotating joint; the lower end of the shell is connected with the rotary connector through a gear ring, and the included angle between the axis of the gear ring and the vertical axis of the shell is larger than 0 degree. The device has the advantages of being simple in structure, convenient to operate, reliable in performance and high in structure bending angle adjustment control precision; the bend angle is adjusted underground, the requirement for track control of a well with a complex structure is met, and the purpose of completing drilling track control of the well with the complex structure with low cost, high efficiency and high precision is achieved.

The invention belongs to the technical field of flight systems, and particularly relates to a six-rotor unmanned aerial vehicle accurate flight system with positioning and navigation functions, which comprises a multi-source positioning sensing module, a dynamic weight fusion module and a self-adaptive trajectory control module, the multi-source positioning sensing module is integrated with a double-antenna RTK-GNSS unit, a triple-redundancy IMU unit, a laser radar SLAM unit, a visual anchor point identification unit and a pipeline electromagnetic detection unit and can collect air-ground-underground multi-dimensional positioning signals at the same time, and each unit is provided with a signal preprocessing sub-module which can perform noise reduction and abnormal value elimination on original signals; the dynamic weight fusion module is internally provided with a signal confidence evaluation model and a scene pre-judgment sub-module, and the signal confidence evaluation model can calculate the confidence value of each positioning unit in real time. According to the system, through multi-module cooperative linkage, the full-scene positioning precision is greatly improved, and the positioning limitation of a traditional unmanned aerial vehicle is effectively solved.

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 belongs to the technical field of communication, and relates to an unmanned aerial vehicle user association and 3D flight path optimization method for anti-interference communication. The method comprises the steps of firstly obtaining information of the unmanned aerial vehicle, each ground node and an interference node to establish a system model, then establishing a 3D trajectory operation model, a user association strategy model and a sight distance probability channel model of the unmanned aerial vehicle, defining a communication rate, and finally establishing an optimization problem of maximizing the total throughput of the system. An optimization problem is modeled as a Markov decision process, finally a user association and unmanned aerial vehicle 3D trajectory planning network is constructed, and an unmanned aerial vehicle learning optimal strategy and an unmanned aerial vehicle 3D trajectory and user association strategy are obtained through a deep learning framework. According to the method, while the lightweight characteristic is maintained, user scheduling and unmanned aerial vehicle 3D trajectory control are dynamically realized, and the communication performance of an unmanned aerial vehicle auxiliary communication system in a malicious interference environment is remarkably improved.

The invention discloses an adaptive control system and method for a dynamic evolution scene of a trackless rubber-tyred vehicle, and the method comprises the steps: firstly collecting surrounding environment information, constructing a dynamic and static spatio-temporal topological graph of an underground roadway, and carrying out the modeling analysis of a dynamic evolution and unstructured underground roadway environment; a multi-sensor sensing information value evaluation system suitable for the underground roadway environment is constructed, and the value of data obtained by each sensor is evaluated; establishing a long-acting path planning scheme of the trackless rubber-tyred vehicle, and realizing self-adaptive long-acting decision of the trackless rubber-tyred vehicle in a dynamic scene; establishing a trajectory prediction scheme in combination with historical trajectory information, and sending a pre-control command to the trackless rubber-tyred vehicle according to the trajectory prediction information; establishing a trajectory control scheme to enable the trackless rubber-tyred vehicle to track the planned trajectory in real time; through the process, the working efficiency of the trackless rubber-tyred vehicle in the underground coal mine is improved, and the running safety of the trackless rubber-tyred vehicle can be guaranteed.

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 invention relates to the technical field of flight safety and flight data analysis, in particular to a last approach artificial flight stage evaluation method based on random forest and K-Means clustering. The method comprises the following steps: intercepting original time series data of a final approach stage from an aircraft QAR record, and calculating kinetic energy and potential energy changes to obtain total energy change data; establishing an evaluation system taking trajectory control and energy management as indexes, calculating importance scores of features of time sequences corresponding to the indexes by using a random forest model, and selecting two features with the highest scores as evaluation sub-indexes; after the sub-index values are standardized, scoring intervals are divided by adopting a K-Means clustering algorithm, and scoring is carried out; according to feature importance score normalization, a weight is obtained, and final scores of glide track control, course track control and energy management indexes are calculated through weighted summation. According to the method, comprehensive and objective quantitative evaluation is realized, and the method is suitable for pilot ability evaluation and flight safety monitoring.

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.

This specification relates to the field of drilling technology, and more particularly to a wellbore trajectory control method, apparatus, and electronic equipment. The method includes: acquiring drilling engineering data during the drilling process, the drilling engineering data including inclination measurement data; calculating initial build-up rate data based on the acquired inclination measurement data; querying historical build-up rate data under the same drilling conditions as the initial build-up rate data; determining the actual build-up rate data based on the historical build-up rate data and the initial build-up rate data; determining the drilling deviation level based on the actual build-up rate data and the designed build-up rate data; and executing a corresponding wellbore trajectory control strategy based on the drilling deviation level. The embodiments of this specification can improve the wellbore trajectory control effect, reduce manual workload, and lower drilling risks.

The invention discloses an improved unmanned aerial vehicle dynamic window obstacle avoidance method based on an artificial potential field method. The method comprises the following steps: selecting local path points from a global path at certain intervals as sub-target path points for guiding local path planning; carrying out sampling combination on the speed space of the unmanned aerial vehicle in the current state, and generating a plurality of feasible candidate trajectories in a time window; introducing an improved artificial potential field function into an evaluation function of a dynamic window method, and comprehensively evaluating each candidate trajectory; and selecting the trajectory control instruction with the optimal comprehensive evaluation score as the motion input of the next control period, and realizing real-time obstacle avoidance and path correction of the unmanned aerial vehicle in a complex dynamic environment. Obstacle information and the positions of the sub-target path points are updated in real time in the flight process, if the distance between the current position and the positions of the sub-target path points is smaller than a set sub-target path point arrival value, it is regarded that the unmanned aerial vehicle arrives at the sub-target path points, and updating is carried out according to the obtained sub-target path points; and updating the next sub-target path point in the sub-target path point set to realize dynamic path adjustment. The device comprises a processor and a memory.

The invention discloses a mechanical arm teaching control method, device and equipment and a medium, and belongs to the technical field of mechanical control. The method comprises the steps that calibration operation is conducted on the basis of a teaching tool file, and a first conversion relation from a mechanical arm base coordinate system to an optical positioning coordinate system and a second conversion relation from a mechanical arm tail end coordinate system to a teaching tool coordinate system are obtained; based on the first conversion relation and the second conversion relation, obtaining a teaching track corresponding to the preset number of teaching operations; each teaching track is converted into a dynamic motion primitive model parameter, and a target teaching track is determined based on each dynamic motion primitive model parameter; and a target mechanical arm joint angle sequence is determined based on the target teaching track so as to control the mechanical arm. According to the technical scheme, complex parameter input or manual physical intervention is not needed in the teaching process, intuition, high precision and high efficiency are considered, and therefore precise track control and stable operation execution of the mechanical arm are achieved.

A tennis training robot serving track control method belongs to the technical field of serving equipment. The problem that an existing pitching machine can not meet the requirements of a user for specific drop points and tracks when pitching according to a preset mode is solved. A corresponding yaw angle, an optimal launching speed and an optimal pitch angle are generated according to input given target drop point coordinates and given target net passing height, and corresponding control signals are generated according to the yaw angle, the optimal launching speed and the optimal pitch angle to control a yaw motor, a friction wheel motor and a pitch motor of the training machine. Therefore, the serving track is controlled at a given target drop point and a given target net passing height. The method is mainly used for ball serving track control of the tennis training machine.