139 results about "Kinematic modeling" patented technology

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention provides an air-sea cross-domain unmanned intelligent cluster collaborative path planning method, and belongs to the technical field of collaborative planning. The method comprises the following steps: S1, carrying out kinematics modeling on a UAV unmanned cluster, a USV unmanned cluster and an AUV unmanned cluster; s2, by taking multiple straight lines and few curves of the path, the length of the total path and the short total consumed time as optimization objectives, constructing a collaborative navigation planning objective function through weighted fusion; and S3, carrying out path planning by adopting a collaborative path planning algorithm based on an improved particle swarm. According to the air-sea cross-domain unmanned intelligent cluster collaborative path planning method, the advantages of different platforms can be fully utilized, various collaborative tasks such as target searching, tracking and disposal are executed in space areas such as low altitude, water surface and underwater, the application range is further expanded, the resource utilization rate is further increased, and the method is suitable for large-scale popularization and application. The method has the advantages of finer modeling and performance, prevents the algorithm from falling into local optimum, and accelerates the convergence speed.

The invention relates to the technical field of automatic laying of composite materials, in particular to an automatic compensation method, system and equipment for errors between silk laying belts and a storage medium. The method for compensating the error between the automatic silk laying belts comprises the following steps: establishing a mathematical mapping relation between the error compensation amount between the belts and robot control parameters through a standard sample piece laying test, and forming an error compensation theoretical model; the method comprises the following steps: aiming at a robot type fiber placement machine structure, constructing an inter-band error model based on pose kinematics modeling, robot and additional axis multi-axis interpolation modeling and error vector decomposition; the tail end target position, the attitude angle and the error compensation amount in the laying program are read in real time, and real-time control input is obtained; and on the basis of the error model, the corrected tail end position is inversely solved into a real-time adjustment angle of each joint, a robot inverse solution algorithm is corrected, and a joint control instruction is generated. According to the automatic tow belt laying device, the laying position precision of the automatic tow belt laying can be remarkably improved.

The invention relates to the technical field of industrial robots, in particular to a five-degree-of-freedom industrial robot path planning method and system based on D-H parameters and dynamics modeling, and realizes high-precision path planning and dynamic environment adaptation by combining kinematics modeling, dynamics analysis and MATLAB simulation technologies. The method comprises the steps of building a robot kinematics model, building a kinetic equation, mixing a path planning strategy, solving inverse kinematics and performing multi-sensor fusion control. The system covers modeling, calculation, planning, simulation and feedback modules, and the operation efficiency and safety of the industrial robot in a complex scene are effectively improved.

The invention relates to the field of exoskeleton robot technology and robot control, in particular to an intelligent self-adaptive exoskeleton robot multi-view sensing obstacle avoidance method and system. A binocular camera is combined with a laser radar to monitor the external environment, and when it is detected that an obstacle exists around a wearer, the obstacle avoidance is performed; and path searching is carried out by using an improved fast expansion random tree star algorithm, a control instruction is generated based on exoskeleton kinematics modeling, and the exoskeleton robot is driven to realize an obstacle avoidance function. The obstacle avoidance method can flexibly deal with various complex conditions in a dynamically changing environment, is high in real-time performance, can help a wearer to complete obstacle avoidance in some emergency conditions, fully reflects the intelligent self-adaptive capability of the system, and enhances the safety and reliability.

The invention discloses a lower limb exoskeleton control system and method based on phase recognition and model prediction control. The system comprises a kinematics modeling module, a gait database module, a data processing and feature extraction module, a gait phase recognition module, a dynamics modeling module, a model prediction controller and an actuator control unit. By recognizing the gait phase and dynamically adjusting and controlling the target, precise assistance of the natural gait of the wearer is achieved. The system calculates the trajectory of the ankle joint relative to the hip joint in real time based on a kinematic model, and constructs a database in combination with gait data collected under multiple working conditions to provide an expected trajectory and reference parameters for control. In the dynamic modeling process, motor driving torque and man-machine interaction torque are introduced, a system dynamic model is established in a unified mode, and control characteristics under different gait phases are reflected.

The invention provides a unilateral step obstacle crossing attitude control method for a wheel-track combined type mobile system, which relates to the field of mechanical engineering control and comprises the following steps of: establishing an initial coordinate system of a robot, and defining space attitude parameters of the robot in an obstacle crossing attitude adjustment process; representing topographic conditions by using the wheel center position, representing a vehicle body attitude by using the spatial attitude parameters, and performing spatial transformation on the initial coordinate system to obtain a kinematic model about the wheel center position and the spatial attitude parameters; on the basis of kinematics modeling, obstacle crossing stability of the robot is used as a control target, the swing angle of a rear swing arm of the robot and the length of an electric push rod are used as control variables, the control variables are used as an initial population of an improved genetic algorithm, and the improved genetic algorithm is used for carrying out iterative solution on the control variables until an optimal solution is obtained. The obstacle crossing posture of the single-side step obstacle of the robot is adjusted by controlling the optimal solution, then the pitch angle and the roll angle are cooperatively adjusted, and the obstacle crossing stability is improved.

The invention discloses a multi-degree-of-freedom path planning method for unsupported additive manufacturing of a suspension structure. According to the method, a to-be-printed model is sequentially subjected to preprocessing, cone transformation processing and slicing processing conversion, and a planned path of a cone transformation model is obtained; sequentially performing path segmentation processing and inverse cone transformation processing on the planned path of the cone transformation model to obtain a planned path of the to-be-printed model under the coordinate system of the to-be-printed model; performing exponential product kinematics modeling on the equipment to obtain a kinematics conversion relation between the coordinate system of the model to be printed and the coordinate system of the equipment; and then conversion is carried out to obtain a planned path of the to-be-printed model under the equipment coordinate system, and 3D printing is carried out. The method is suitable for most of existing slicing software, the unsupported printing overhanging structure can be realized by utilizing the generated slicing software path planning, so that the processing process after 3D printing is simplified, and the quality and the surface flatness of a printed piece are further improved.

The invention relates to a mechanical arm control information generation method and device and a surgical robot. The method comprises the following steps: determining a target operation position; through kinematics modeling, motion control information of the mechanical arm is determined based on the target operation position; inputting the motion control information into a pre-trained neural network model to obtain a predicted operation position corresponding to the motion control information; on the basis of the target operation position and the predicted operation position, under the condition that it is determined that the predicted operation position does not meet the requirement, the motion control information is adjusted, the adjusted motion control information is input into the neural network model, and the predicted operation position corresponding to the motion control information is obtained till the motion control information is obtained on the basis of the target operation position and the predicted operation position. And under the condition that it is determined that the predicted operation position meets the requirement, the final adjusted motion control information serves as control information of the mechanical arm. By adopting the method, the control precision of the mechanical arm can be improved.

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

The invention belongs to the technical field of mechanical arm joint control, and particularly discloses a preset performance self-adaptive joint control method for a rope-driven super-redundant snakelike mechanical arm, and the method comprises the following steps: carrying out kinematics modeling on a rope joint link of the rope-driven super-redundant snakelike mechanical arm, determining a relation between a rope speed and a joint angle, and determining a preset performance of the rope-driven super-redundant snakelike mechanical arm; calculating a joint jacobian matrix; designing a joint angle controller, defining an angle error and constraining the angle error in a preset performance range, and obtaining control input through an error transformation and return-to-zero dynamics principle so as to realize joint angle control; according to the method, a joint Jacobian matrix is estimated on line, and the joint Jacobian matrix is dynamically updated according to a rope speed error and a return-to-zero dynamic theory so as to adapt to the flexibility, time-varying characteristics and external disturbance of a rope, and the control precision is ensured. According to the method, it can be guaranteed that the joint angle of the rope-driven super-redundant mechanical arm can be accurately controlled under different working conditions, and it is guaranteed that the mechanical arm completes high-precision operation under the complex working conditions.

The invention belongs to the technical field of electric forklift modeling, particularly relates to a kinematics modeling method and system for an electric forklift, and aims to solve the problem that the accuracy of forklift kinematics modeling cannot be guaranteed due to the fact that environmental parameters, electrical load data and battery data of the forklift are inconvenient to comprehensively collect in the prior art. According to the scheme, the method comprises the following steps that S1, forklift environment data and forklift motor parameter data are set firstly, and the set data are collected and sorted; s2, forklift whole vehicle data, electrical load data and battery data are collected, and the collected data are sorted and uploaded; and S3, recording all the collected data, in the using process, the environmental parameters, the electrical load data and the battery data of the forklift can be conveniently and comprehensively collected, then the accuracy of forklift kinematics modeling can be effectively improved, and a solid foundation is provided for forklift optimization.

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

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

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

The present disclosure discloses embodiments of a kinematic modeling method and computer readable non-transitory storage medium for predicting the evolution of a submarine salt sedimentary layer. An embodiment of a method includes the steps of: generating a reference map for the layer thickness in the analyzed geological event; determining reference geometry for the base surface of the layer in the analyzed geological event; determining geometry of the top surface of the layer in the analyzed geological event; and calculating the positioning of the top surfaces and adjustment of the global volume of the layer.

The invention discloses a universal dual-mode active-disturbance-rejection control method for an air-ground amphibious robot. The method comprises the steps that kinematics modeling is conducted on a flight end and a ground end according to the structure of the air-ground amphibious robot; carrying out ground-air double-end common disturbance pre-modeling; constructing an extended state observer; detecting a current motion mode; weighting a nonlinear least square model, compensating disturbance information, and estimating total disturbance in real time; and a bimodal active-disturbance-rejection controller is designed, disturbance is compensated in real time, and bimodal active-disturbance-rejection control over the air-ground amphibious robot is achieved. According to the method, information of all motion mode models and common disturbance of the air-ground amphibious robot are preprocessed, the improved active-disturbance-rejection control method is applied under dual modes, the adaptive capacity of the air-ground amphibious robot to external disturbance and complex environments during reconnaissance work is greatly improved, and the method has universality in control over the air-ground amphibious robots of various mechanical structures.

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

The invention relates to the technical field of intelligent control, and particularly to a gantry platform kinematics modeling method considering parallelism and perpendicularity errors, which is applied to a gantry platform device. The gantry platform device comprises a crossbeam, a first guide rail and a second guide rail, one end of the crossbeam is in sliding fit with the first guide rail, and the other end of the crossbeam is in sliding fit with the second guide rail; the first guide rail is provided with a first longitudinal motor, and the second guide rail is provided with a second longitudinal motor; and the crossbeam is provided with a transverse motor configured to drive the gantry platform to move along the crossbeam. The method comprises: establishing a two-dimensional coordinate system; constructing a forward kinematics solution model; and constructing an inverse kinematics solution model.

The invention relates to the technical field of neural network optical fiber measurement, and discloses a soft mechanical arm kinematics modeling method based on a convolutional neural network, which comprises the following steps: step 1, optical fiber curvature reconstruction: detecting the curvature and the curvature deflection angle of a soft mechanical arm through an optical fiber bragg grating sensor; when the soft mechanical arm is bent, the center wavelength of the fiber bragg grating sensor drifts, and curvature and angle information of an optical fiber is calculated based on the drifting distance. 2, three-dimensional position coordinates of the soft arm are obtained; step 3, training a convolutional neural network; and 4, the kinematics of the soft mechanical arm is predicted. Inverse kinematics modeling of the soft arm can be completed only on the basis of curvature information obtained from a fiber bragg grating (FBG) sensor and position information obtained from a visual sensor by adopting a convolutional neural network mode, and the soft arm inverse kinematics modeling method has very high generalization ability and is suitable for soft mechanical arms of different shapes, sizes and materials.

The invention relates to the technical field of bionic mechanical arms, in particular to a rigid-flexible coupling line driven twenty-degree-of-freedom bionic dexterous hand device which comprises a far-end knuckle, a middle-end knuckle, a near-end knuckle, palm fingers, an air bag, a palm support, a ball pair, a connecting piece, a control part support, a first connecting assembly, a second connecting assembly, a third connecting assembly and a control assembly. The far-end knuckles and the middle-end knuckles are connected through first connecting assemblies, the near-end knuckles and the middle-end knuckles are connected through second connecting assemblies, the palm fingers and the near-end knuckles are connected through third connecting assemblies, the palm conducts folding movement through inflation and deflation of an air bag, and through the rigid-flexible coupling design, complete decoupling of the degree of freedom is achieved; all the joints can move completely and independently, the joints do not influence one another, kinematics modeling can be conducted in the driving process, the motion control compensation amount of all the driving steering engines is calculated, and the adaptability and flexibility of the driving steering engines in the complex environment are improved.

The invention discloses an unmanned aerial vehicle cluster formation fault-tolerant method based on protopigeon chaos adaptive sliding mode control, and the method comprises the steps: 1, carrying out the kinematics modeling of an unmanned aerial vehicle, and determining the initial state of the unmanned aerial vehicle and a formation flight control system of wing aircrafts and lead aircrafts; 2, introducing a virtual reference unmanned aerial vehicle kinetic model, designing a tracking error, obtaining a dynamic tracking error equation, and ensuring the accuracy of a reference model; 3, designing a sliding mode surface function, and obtaining a reduced-order sliding mode kinematics equation by applying an adaptive sliding mode control technology; and the like. The method is easy to implement, the provided chaotic pigeon inspired mechanism can greatly optimize parameters in adaptive control and sliding mode control parameters, the control convergence speed is increased, and a robust solution is provided for solving the difficult problems that an unmanned aerial vehicle with communication faults cannot adapt to a complex environment and the like.

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

The invention provides a safety management system and method for an offshore wind turbine generator, and relates to the technical field of offshore wind power. Comprising a meteorological sensing subsystem, a wave analysis subsystem, a dynamic modeling subsystem and a safety regulation and control subsystem, wherein the meteorological sensing subsystem comprises a wind speed monitoring module, an air pressure monitoring module, a temperature and humidity monitoring module and a data fusion module; the wave analysis subsystem comprises a wave height monitoring module, a wave period monitoring module, a wave direction monitoring module and a wave coupling calculation module; the dynamic modeling subsystem comprises a kinematics modeling module, a dynamics modeling module, a wind wheel load calculation module and a platform stability evaluation module; the safety regulation and control subsystem comprises a wind speed sudden change response module, a wave coupling optimization module, a platform posture adjustment module and an emergency shutdown module. According to the scheme, the operation safety of the offshore wind turbine generator is improved.

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

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

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

The invention relates to the technical field of robot man-machine interaction, in particular to a kinematics analysis method for a new-configuration six-degree-of-freedom force feedback hand controller, and the method comprises the steps: constructing a joint coordinate system of each rod piece of the hand controller; obtaining D-H parameters of a joint and a rod piece in the hand controller; performing joint processing on the kinematic model of the hand controller; obtaining a conversion matrix between adjacent joints; and the pose of the end effector of the hand controller in the base coordinate system is obtained. According to the method, kinematics modeling is carried out on the six-degree-of-freedom force feedback hand controller which is subjected to shaft shifting processing and added with the virtual joints by utilizing a D-H method, so that the six-degree-of-freedom force feedback hand controller obtained through modeling is more accurate.

The present invention discloses a multi-degree-of-freedom path planning method for unsupported additive manufacturing of a hanging structure. The present invention sequentially performs preprocessing, conical transformation processing, and slicing processing on the model to be printed to convert and obtain the planned path of the conical transformation model; sequentially performs path segmentation processing and inverse conical transformation processing on the planned path of the conical transformation model to obtain the planned path of the model to be printed in the coordinate system of the model to be printed; performs exponential product kinematic modeling on the device to obtain the kinematic conversion relationship between the coordinate system of the model to be printed and the coordinate system of the device; and then performs conversion to obtain the planned path of the model to be printed in the coordinate system of the device and performs 3D printing. The present invention is applicable to most existing slicing software, and can realize unsupported printing of hanging structures by using the path planning of the generated slicing software, thereby simplifying the post-processing process after 3D printing and further improving the quality and surface flatness of the printed parts.

The invention discloses a kinematics semi-analytical modeling method and terminal for a human wrist-simulated parallel mechanism, and the method comprises the steps: constructing a global coordinate system, a first coordinate system and a second coordinate system when the wrist parallel mechanism is subjected to kinematics positive solution, and then obtaining a target D-H expression corresponding to the tail end pose of the human wrist-simulated parallel mechanism, a target D-H expression is obtained, the target D-H expression shows the relation between the tail end pose and a first joint angle and the relation between the tail end pose and a second joint angle, then a target modular length expression is obtained, the target modular length expression shows the relation between a first modular length and the first joint angle and the relation between a second modular length and the second joint angle, and finally, the target D-H expression shows the relation between the tail end pose and the second joint angle. And obtaining numerical values of a first modulus length and a second modulus length, and substituting the numerical values of the first modulus length and the second modulus length into the target D-H expression and the target modulus length expression to obtain a tail end pose. The invention provides a solution capable of completing operation on a low-computing-power platform for a parallel mechanism simulating a human wrist.