3414 results about "Industrial robotics" patented technology

The invention relates to the technical field of industrial robots, in particular to an industrial robot disordered grabbing system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module, a pose estimation and correction module, a grabbing task planning module and a mechanical arm execution module. The multi-modal sensing module is used for acquiring RGB image data, depth image data, point cloud data and force / torque data in a working environment, and the pose estimation and correction module performs feature extraction and fusion on multi-modal data based on topology invariant mapping and performs adaptive pose estimation on a target object. The grabbing task planning module generates an optimal grabbing strategy based on deep reinforcement learning and outputs a grabbing control instruction, and the mechanical arm execution module receives the grabbing control instruction, controls a mechanical arm to execute a grabbing action and monitors a grabbing state through force / torque feedback. And the environmental adaptability and the perception robustness are improved.

The invention provides a degradable plastic production method which comprises the following steps: acquiring an optimal raw material proportioning scheme, and controlling an intelligent batching system to perform automatic batching; temperature and humidity parameters of raw material storage and conveying links are obtained, and raw material pretreatment process parameters are dynamically adjusted in combination with time sequence data analysis; performing quality detection on the preprocessed raw materials, identifying defects and impurities of the raw materials by adopting machine vision and a deep learning algorithm, and removing unqualified raw materials; various raw materials are accurately weighed through an industrial robot and mixed according to the optimized formula proportion; a thermal infrared imager is adopted to monitor temperature field distribution of a plastic extrusion forming mold in real time, and finite element simulation analysis is combined to optimize mold structure design and a heating and refrigerating system; the extrusion molding process is monitored on line, material component changes are analyzed through a chemometrics model, and if degradation products exceed the standard, extrusion process parameters are adjusted or a mold is replaced.

In one aspect, the present invention relates to a distributed system for controlling at least one robot (R) in a gripping task for gripping objects (O) of different types which are arranged in a working area (CB, B, T) of the robot (R). The system comprises a central training computer (CTR), which is designed for pre- and post-training, and at least one local processing unit (LCU), on which real image data of the object (O) is recorded, which is used to generate post-training data.

The invention relates to the field of industrial robots, and discloses an industrial robot real-time maintenance system and method in combination with edge computing, and the method comprises the steps: obtaining the multi-source operation state data of an industrial robot, and constructing an operation feature data set of key parts of the robot in combination with a state coding mechanism of an edge end and a feature coupling analysis method; carrying out rapid distributed processing on the operation characteristic data set, constructing an equipment health state model based on a lightweight time sequence modeling algorithm, and introducing a multi-dimensional correlation analysis mechanism to carry out incremental learning on the model; judging whether the edge side state recognition result is stable or not based on the change trend of the trigger frequency; according to the corrected state mapping relation, performing response level division on the potential fault trend by applying a multi-scale fault prediction mechanism, and extracting matched maintenance plan parameters; and based on the maintenance scheduling plan, in combination with a preset fault handling knowledge base, performing automatic evaluation and optimization on the current maintenance strategy. The method has the advantage of improving the response speed.

The invention relates to the technical field of industrial motor faults, in particular to an industrial robot motor fault early warning method and system. The method comprises the following steps: acquiring a motor control instruction of the industrial robot in real time, and then judging an instruction value change event to obtain a motor load change event; monitoring transient working condition characteristic data of the motor in real time according to the motor load change event; flexible gear jump response abnormity evaluation is carried out based on the motor transient state working condition characteristic data, and a flexible gear comprehensive response abnormity score is generated; electromagnetic fault feature analysis is carried out according to the transient working condition feature data of the motor to obtain electromagnetic fault feature coupling data; and according to the electromagnetic fault feature coupling data and the flexible gear comprehensive response abnormal score, performing motor flexible gear health state analysis so as to realize industrial robot motor fault early warning. According to the invention, through working condition jump point detection and motor flexible gear health state analysis, the motor fault can be accurately identified, and multi-stage fault early warning is realized.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention provides a self-adaptive teaching-free welding robot intelligent gas saving monitoring system, which relates to the technical field of industrial robots, and comprises a three-dimensional sensing module used for identifying the workpiece material category through a multispectral sensor integrated with a welding gun of a welding robot, meanwhile, a stereoscopic vision camera and a line structure light scanning camera are used for obtaining three-dimensional point cloud data of the welding seam in real time; the parameter adjusting module is used for calling a heat conductivity coefficient database based on the three-dimensional point cloud data, and correcting the initial flow of the welding current, the arc voltage and the protective gas in real time; and the matching control module is used for calculating the arc length variable quantity based on the arc voltage, controlling the opening degree of the gas flow valve through a dynamic regulator, realizing dynamic matching of the gas flow and the changed arc length, and generating gas dynamic matching control parameters. Through cooperative operation of self-adaptive attitude compensation and real-time gas consumption monitoring, teaching-free accurate operation in the welding process is achieved, and intelligent control over gas consumption is improved.

The invention provides a robot teleoperation method and system based on virtual reality and digital twinning, and particularly relates to the technical field of man-machine collaborative intelligent manufacturing and assemblation.The scheme comprises the steps that an industrial robot and wearable equipment are started; establishing a two-way communication connection between the virtual environment of the wearable device and the industrial robot, and rendering the virtual robot of the wearable device in the virtual environment; and inputting the target pose operation instruction into the virtual robot, responding to a received target motion track planned by the wearable device according to the target pose operation instruction, and controlling the industrial robot to execute an operation task according to the motion track. According to the scheme, information interaction between an operator and the robot can be achieved, so that immersive real-time robot teleoperation is achieved through instant and accurate operation commands and field feedback, and the teleoperation precision of complex tasks and flexible operation tasks can be remarkably improved.

The invention provides a bearing for an industrial robot, and belongs to the technical field of industrial robot bearings. The inner ring is arranged between the inner walls of the outer ring, and a roller path is formed in a gap between the inner ring and the outer ring; a plurality of rollers are arranged, the plurality of rollers are distributed between the inner walls of the roller path in a crossed manner, and spacing blocks are filled among the plurality of rollers; the adjusting mechanism is arranged between the outer ring and the inner ring, the multiple rollers are carried by the multiple spacer blocks to perform central radial movement in the roller path, and the multiple spacer blocks drive the multiple rollers to be uniformly dispersed between the inner walls of the roller path, so that the multiple spacer blocks perform central axial distribution adjustment in the roller path, and the gap positions between the rollers are adjusted; and the rollers are connected to the inner ring and are better attached to the outer ring, so that a roller path is fully filled, the outer ring and the inner ring are radially coaxial, and high-decibel noise generated by collision and friction of the outer ring and the inner ring during high-speed rotation when the outer ring and the inner ring of the bearing rotate at a high speed is avoided.

The invention discloses an industrial robot cluster operation state intelligent monitoring and prediction maintenance system, which comprises a cloud main system and a plurality of subsystems, and is characterized in that the subsystems are arranged corresponding to regional robot clusters; the cloud main system comprises a core platform layer, a communication layer, a logic layer and a storage layer; each subsystem comprises a regional communication layer, a regional logic layer and a regional storage layer, and the regional communication layers are used for realizing data transmission in the subsystems, among the subsystems and between the subsystems and the cloud main system; the regional logic layer is internally provided with a data preprocessing module, a multi-source data fusion alignment module, a regional anomaly monitoring module, a robot regional collaboration and optimization module and a predictive maintenance module. The industrial robot cluster operation state intelligent monitoring and prediction maintenance system aims at improving cluster monitoring and maintenance efficiency, reducing non-planned shutdown and guaranteeing stable and efficient operation of a production line.

The invention relates to the technical field of industrial automatic welding and discloses an intelligent control method and system for industrial robot welding, and the method comprises the steps that after a welding task is started, an initial welding process parameter model is called based on the current workpiece specification, and a first welding control instruction sequence is generated; according to the first welding control instruction sequence, a welding gun is controlled to execute welding operation, and multi-source sensing data flow in the welding process is synchronously collected; carrying out fusion feature extraction on the real-time sensing data stream to generate a current welding situation feature vector, when situation deviation exists between the current welding situation feature vector and the reference welding situation feature vector, obtaining a self-adaptive welding process parameter model through online incremental learning, and obtaining a self-adaptive welding process parameter model on the basis of the self-adaptive welding process parameter model. And generating an optimized welding control instruction sequence and issuing the optimized welding control instruction sequence to a robot controller and a welding gun so as to execute self-adaptive welding operation. The welding quality stability and the production efficiency can be improved, and the equipment maintenance cost is reduced.

The invention belongs to the field of industrial robot error calibration, and particularly relates to a vision-based industrial mechanical arm zero deviation calibration method, which comprises the following steps of: 1) fixing a vision camera at the tail end of a mechanical arm, and fixing a marker in a working area range of the mechanical arm; (2) the mechanical arm is manually operated, the spatial position of a camera fixed to the tail end of the mechanical arm is changed, all corresponding pose data are recorded, the mechanical arm is moved multiple times, and multiple sets of calibration data are collected; (3) the pose change of the marker when the spatial position of the camera fixed to the tail end of the mechanical arm is changed is obtained, and a calibration equation based on zero deviation and hand-eye pose deviation is constructed; and (4) multiple sets of collected calibration data are substituted into the calibration equation, and after optimization is conducted, the zero deviation of the mechanical arm and a hand-eye pose transformation matrix are obtained and compensated to configuration of a mechanical arm controller. According to the method, the zero deviation calibration of the mechanical arm and the calibration of the hand-eye pose transformation between the mechanical arm and the visual equipment can be completed at the same time only through one calibration process.

The invention relates to the technical field of automatic equipment control, in particular to an automatic equipment control system and method and a storage medium. The method comprises the following steps that a high-frequency laser scanner is used for scanning the surface of a target polishing part, B-spline surface fitting is carried out, and a surface fitting surface model is generated; controlling the polishing operation of the polishing industrial robot based on the surface fitting curved surface model, and performing polishing contact state detection to generate contact state point cloud data; based on the contact state point cloud data, grinding expected pose error analysis is carried out, reverse impedance error compensation is carried out, and a reverse impedance compensation amount is obtained; and the joint angle of the polishing industrial robot is corrected through the reverse impedance compensation amount, so that reverse impedance control over the automatic polishing equipment is achieved. By compensating the reverse impedance error of the automatic polishing equipment, accurate control over the automatic polishing process is achieved.

The invention provides a machine grabbing learning method fusing shape features, contact modeling and physical constraints, and the machine grabbing learning method is a robot grabbing posture learning method capable of differentiating. According to the method, a three-dimensional point cloud serves as input, a deep neural network based on sparse voxel convolution is adopted to extract multi-scale shape features, and potential grabbing points and attitude parameters thereof are predicted. The differential contact modeling is realized by constructing a local area of direction perception and extracting geometric features reflecting the contact relationship between the mechanical claw and the object. Furthermore, the collision probability of the grabbing posture is estimated based on an implicit neural network, and a differentiable collision detection module is constructed. Meanwhile, physical constraints such as friction closing, surface alignment and geometric symmetry are introduced to serve as regular terms to jointly optimize grabbing scores, collision risks and stability; according to the method, end-to-end training is supported, efficient, robust and deployable grabbing posture estimation is achieved, and the method is suitable for various automatic grabbing tasks such as industrial robots and service robots.

The invention relates to the technical field of industrial robot path planning, in particular to an industrial mobile robot path planning method based on an artificial potential field method and dynamic prediction. The method comprises the following steps: firstly, acquiring environment point cloud data, and distinguishing static and dynamic obstacles based on time sequence difference and Euclidean distance; kalman filtering is used to predict the future position of a dynamic obstacle, and a density clustering algorithm is used to simplify a static obstacle; combining a target point, a static obstacle set and a dynamic obstacle prediction result to construct an improved artificial potential field function to calculate gravitational force and repulsive force; the potential collision risk is judged based on the motion state of the robot, the dynamic obstacle repulsive force with the risk is brought into potential synthesis in advance, and the final resultant force for guiding motion is generated; and when it is detected that the system is caught in a local optimal or oscillation state, executing a virtual gravity point escape strategy until the system reaches a target point. According to the method, the fusion of static clustering, dynamic prediction and potential field improvement is realized, and the obstacle avoidance and path planning efficiency in a complex dynamic environment is improved.

The invention relates to the technical field of industrial robot path control, and discloses an industrial robot real-time path correction method, which comprises the following steps: acquiring current position information, speed information and acceleration information; calculating and generating a path error value by adopting an Euclidean distance; an error feedback control method and an inverse kinematics algorithm are combined to generate a correction matrix, and the angle, angular velocity, acceleration and angular acceleration parameters of the industrial robot joint are adjusted in real time; and further adjusting the motion trail parameters based on an improved Kalman filtering algorithm. Compared with the technical problems that in the prior art, path correction lags behind, precision is insufficient, and real-time path correction is difficult to achieve especially in a complex dynamic environment, due to the fact that multi-sensor fusion, a feedback control algorithm and an improved PID control algorithm are combined for use, the path is corrected in real time, error accumulation is reduced, and therefore the situation that path deviation is too large is avoided; and the production efficiency of the industrial robot in a complex environment is improved.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The multi-interaction-mode intermediate frequency furnace automatic slag salvaging mechanism comprises a guide rail, an industrial robot and intermediate frequency furnaces, the guide rail is used for bearing the industrial robot, the guide rail is installed between the two intermediate frequency furnaces, furnace openings of the two intermediate frequency furnaces are each provided with a furnace cover, and the interiors of furnace bodies of the intermediate frequency furnaces are used for storing molten iron; a coaxial slag salvaging mechanism is arranged at the tail end of the industrial robot, a slag salvaging spoon is arranged at the bottom of the coaxial slag salvaging mechanism, the slag salvaging spoon is used for salvaging slag, and a slag hopper used for storing the slag is arranged at the bottom of the slag salvaging spoon. The rotation angle of the stepping motor is controlled by an instruction sent by the control system, opening and closing of the slag salvaging spoon can be further controlled, the motion mode of the slag salvaging mechanism is unique and different from the prior art, the slag salvaging mechanism can salvage the slag to the maximum extent, and the problem that in the prior art, the slag salvaging spoon needs to be replaced every day is solved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention belongs to the field of industrial automation, and provides a system based on multi-mode perception and intelligent regulation and control. By integrating various sensors on the robot and applying technologies such as multi-modal sensing fusion, bionics, real-time feedback, multi-source data fusion, reinforcement learning and block chains, accurate grabbing and sorting of food materials, dynamic regulation and control of parameters in the processing process, online quality monitoring, multi-robot collaborative scheduling and quality tracing are achieved. The invention aims to solve the problems of difficult material grabbing, inflexible parameter regulation and control, inaccurate quality monitoring and the like in food processing, improve the automation and intelligence level of the food processing industry, and meet the production requirements of high efficiency, safety and high quality.

The invention relates to the technical field of heavy-load industrial robots, and provides a heavy-load industrial robot path planning and safe and accurate control method, which comprises the following steps: acquiring environment topological data, mechanical arm kinetic parameters and real-time sensing data, performing multi-source information fusion on the environment topological data, the mechanical arm kinetic parameters and the real-time sensing data, and obtaining a path planning result; acquiring a spatial state vector; constructing a target attraction item, an obstacle rejection item, a torque constraint item and an action smoothing item; constructing a reward function according to the spatial state vector, the target attraction item, the obstacle rejection item, the torque constraint item and the action smoothing item; and constructing a target function of model prediction control according to the reward function, and optimizing the joint correction according to the target function. According to the method, multi-source sensor information such as visual sense and force sense can be effectively fused, balance of obstacle avoidance and load efficiency and multi-target collaborative optimization are achieved, and therefore safe and accurate control over heavy-load industrial robot path planning is achieved.

The invention discloses a multi-modal self-adaptive clamping system for assembling key components of a humanoid robot and a control method of the multi-modal self-adaptive clamping system, and the system comprises a multi-modal sensing module which is used for collecting assembling parameters; the edge calculation module is used for generating a control instruction by utilizing a multi-mode deep learning model according to the assembly parameters; the cloud service module is used for generating distillation data according to the assembly parameters; the self-adaptive clamping module is used for clamping and moving key components of the humanoid robot; and the programmable logic control module is used for controlling the self-adaptive clamping module to execute component assembling operation according to the control instruction and the distillation data. According to the invention, a multi-mode self-adaptive clamping system is realized, and the efficiency and the clamping stability are improved. The robot can be widely applied to the technical field of industrial robots.

The invention discloses a digital twin calibration framework construction method based on a Lyapunov strategy, and the method comprises the steps: constructing a digital twin model containing a proxy network to simulate the dynamic state of a system, and defining a state, an action space and a reward function through a Markov decision process. A constrained Lyapunov action-commentator (CLAC) algorithm is introduced, a strategy network and a Lyapunov network are optimized, and the algorithm can stably act under high noise and deviation. Real-time parameter optimization is realized by means of single-time neural network forward propagation, an experience playback pool and the like. Each network structure is clear, and a specific initialization and optimization method is adopted. According to the method, a calibration problem can be converted into a parameter tracking task, efficient training is performed under unmarked data, constraint requirements such as stability can be met, and the method is suitable for scenes such as industrial robot joint control.

The invention relates to the technical field of industrial robot control, and discloses a robot end trajectory optimization method and system based on industrial vision, and the method comprises the steps: obtaining a workpiece surface image in real time through a vision sensor, and obtaining deformation data through image enhancement and feature extraction; when the deformation quantity exceeds a threshold value, calculating a multi-axis coordination parameter by adopting an optimization algorithm to generate a trajectory correction instruction; integrating speed constraints by updating a control model, and determining a final trajectory by using Kalman filtering to fuse sensor feedback data; and the control parameters are iteratively adjusted in the deformation feedback loop, and a stable machining track is formed. The method can solve the problem that in the prior art, the robot tail end track precision is low.

The invention relates to a speed planning optimization method and system for industrial robot repeated path operation, and the method comprises the steps: carrying out the constraint iterative learning speed planning based on path arc length coordinates, building a precise rigid-flexible coupling dynamic model of a robot, and obtaining the motion constraint condition of the robot; calculating the speed planning motion performance of the robot through the motion performance evaluation function, and updating the parameters of the motion performance evaluation function based on a dynamic linear scaling mechanism; an intelligent iterative learning mechanism based on arc length coordinate parameterization, dynamic constraint on-line satisfaction and provable monotonic convergence performance is realized through an updating law of an iterative learning motion performance evaluation function; and outputting the optimal speed curve of the robot. Track deviation, vibration data and energy consumption characteristics are automatically recorded when a task is executed each time, the root of a problem is autonomously analyzed, key parameters such as an acceleration curve and joint torque distribution are dynamically adjusted, and dependence on manual parameter adjustment in repeated operation is eliminated.

The invention discloses a high-strength cable for an industrial robot and a preparation process of the high-strength cable. The preparation process comprises the following steps: selecting a high-strength conductor material, performing multi-pass wire drawing processing on the conductor material, and twisting a plurality of fine copper wires according to a preset pitch and direction to form a conductor core; fluoroplastic is selected as a first layer of insulating material to coat the conductor core to form a first insulating layer, and modified thermoplastic elastomer particles coat the first insulating layer to form a second insulating layer; wrapping a wrapping material outside the second insulating layer by using a wrapping machine to form a wrapping layer; a sheath material extruder is provided to wrap the wrapping layer to form an outer sheath, and then cutting is carried out to obtain the high-strength cable. According to the process, material selection and structural design are innovatively combined, the double-layer insulation structure provides excellent high-temperature resistance and electrical insulation performance, friction between the inner layer and the outer sheath is effectively reduced due to application of the wrapping layer, the service life of the cable is prolonged, and therefore the high-strength cable is prepared.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention discloses an industrial robot predictive maintenance method and system based on multi-source data fusion, and the method comprises the steps: synchronously collecting vibration, current, temperature, acoustic and visual signals through multiple types of sensors, carrying out the filtering, correction and normalization processing, and constructing a multi-modal feature set; cross-modal alignment is realized through time compensation, after dimensionality reduction, a mechanical vibration group, an electrical performance group, a thermodynamic group and a motion precision group are divided, mahalanobis distances of the groups are calculated based on a historical health reference to serve as local anomaly degree scores, weights are dynamically adjusted according to the change rate, and the weights are combined into a preliminary health index. And introducing a nonlinear amplification mechanism to enhance high-value response, adaptively switching smooth intensity according to a degradation trend, and outputting a comprehensive health index. According to the method, comprehensive perception and dynamic evaluation of the operation state of the practical training platform are realized, multi-source heterogeneous information is effectively fused, the limitation of single signal monitoring is overcome, and the anomaly recognition accuracy is remarkably improved.

The invention discloses a robot grinding surface roughness prediction method considering dynamic factors based on deep learning, and relates to the technical field of industrial robots. The method comprises the following steps: carrying out self-extraction on grinding machining dynamic factor spatial features by adopting a convolutional neural network; performing time sequence feature extraction on the extracted space by adopting a bidirectional long-short-term memory network; carrying out standardization and normalization processing on the extracted space and time sequence features and static factors; utilizing an improved whale algorithm to optimize hyper-parameters of the bidirectional long-short-term memory network, and further introducing an attention mechanism to realize feature automatic weight distribution; the steps are integrated, and an IWOA-CNN-BiLSTM-Attention surface roughness prediction model is constructed; and inputting the static factors, the extracted features and the surface roughness measurement value into a prediction model for model training, and outputting a surface roughness prediction value to realize a surface roughness prediction function. The method can solve the problem of difficulty in dynamic factor feature selection, and improves the model prediction precision.

The invention discloses a six-axis industrial robot kinematics calibration method based on sub-space identification, and belongs to the field of robot kinematics calibration. The robot is controlled to carry out three-axis linkage and six-axis linkage and move according to the planned four S-shaped paths and one planar circular path, and the error of the robot at the end effector is obtained through measurement of a QC20-W ball rod instrument. And a sub-space identification method is provided to identify and obtain a robot kinematics error parameter, and the error is compensated to a robot kinematics model, so that kinematics calibration of the six-axis industrial robot is realized. And after calibration, the precision is better than that of a whole six-axis linkage compensation method, and a new method is provided for kinematics calibration of the six-axis industrial robot.