1915 results about "Industrial robotics" patented technology

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 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 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 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 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 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 relates to the technical field of robot control, and discloses an industrial robot adaptive control method and system based on multi-modal sensor fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the time-space alignment; capturing space-time semantic association of visual textures, tactile pressure distribution and force sense fluctuation in the multi-modal data through a multi-head attention mechanism guided by a physical model, and performing space-time registration; a CNN-LSTM hybrid model is adopted to extract visual texture features and time sequence tactile features in the physical information enhanced multi-modal feature matrix; and carrying out dynamic weight distribution on the fusion feature vectors with physical consistency by utilizing a weight distribution model driven by element reinforcement learning to generate dynamic weighted fusion features. According to the method, the spatial positioning precision of the industrial robot in a precise assembly scene is greatly improved, the contact force control stability is greatly improved, and the control robustness in a complex operation scene is remarkably enhanced.

According to the embodiment of the invention, the industrial robot technology is applied to the rolling forming process of the variable cross-section part, and the rolling path can be dynamically adjusted according to the variable cross-section characteristics of the part, so that efficient machining is realized. According to the technology, the limitation of an existing technology in complex geometrical shape machining is broken through and solved, and an industrial robot can conduct high-quality and high-precision rolling forming on the variable cross-section part of a complex structure under the condition that the industrial robot does not depend on a mold. In addition, by combining the flexible path control capability of a robot, the forming precision of the variable cross-section part is remarkably improved, and compared with a raw material, the mechanical property loss of a bending area material of the part obtained after forming through the technology is smaller than 30%.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is 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.

Systems and methods relating to automated machining. A machining tool is controlled by an industrial robot that guides the tool around the contour of a workpiece. As the tool traverses the estimated contour of the workpiece, the tool machines the surface while a force sensor provides force readings, the force readings being readings of the force exerted by the tool on the surface. The path followed by the tool around the contour of the workpiece is adjusted based on the force readings. The speed at which the tool traverses the workpiece may be modulated and the path may be adjusted based on estimated tool wear.

The invention provides a servo press control system and method based on a time-sensitive network, and relates to the technical field of intelligent manufacturing and industrial automation control, and the system comprises a time-sensitive network TSN which is used for providing global time synchronization and deterministic data transmission; a main controller of the multi-axis servo control unit is used for issuing a motion control instruction and a feeding and discharging control instruction through a TSN. A robot controller in the robot feeding and discharging unit is used for driving an industrial robot to execute plate taking and placing operation under the action of the feeding and discharging control instruction. The visual monitoring unit comprises an industrial camera accessed to the TSN and is used for collecting images and transmitting image data to the edge computing node through the TSN; the edge computing node is used for processing the image data to generate a compensation instruction and feeding back the compensation instruction through the TSN; and the upper monitoring system is used for subscribing real-time data published by the edge computing nodes, so that the control range of the servo pressure control system is expanded, and the convenience of data transmission and interaction is improved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention relates to an industrial robot health state evaluation method based on multi-modal servo information mapping. The method comprises the following steps: obtaining the influence of a complex service condition on the performance of an industrial robot and a degradation mechanism; health perception features are extracted from multi-mode servo information collected in the operation process of the industrial robot; according to the collected health perception characteristics, a health perception model of the industrial robot under the complex working condition is established; an incremental learning model is established, a degradation model is obtained along with continuous addition, continuous learning and updating of new data, failure time distribution of the degradation model is predicted, decision support is provided for maintenance of industrial robot equipment, and a multi-modal servo information mapping model is constructed by deeply analyzing a performance degradation mechanism of an industrial robot under complex working conditions. And accurate sensing of the health state of the robot is realized. And a self-supervised contrast representation learning theory is utilized to solve the problem of fault sample deficiency, and the accuracy of health perception is improved. Based on an incremental learning theory of multi-modal servo information, an autonomous diagnosis model is established, and rapid diagnosis and performance degradation prediction of a new fault are realized.

The invention discloses a continuity control system for an industrial robot, and relates to the technical field of industrial automation, and the system comprises a spraying behavior recording module, an interruption recognition module, a recovery trajectory planning module, an inertia recovery control module, a spraying recovery execution module and a coating uniformity evaluation module. By recording a spraying state before interruption and identifying an interruption event, the system constructs an interruption influence area and recovers a spraying track; an inertia recovery section is arranged before recovery execution, and smooth transition between the motion state and the spraying state of the end effector is achieved; and after spraying is completed, the coating thickness of the area before and after interruption and the optical consistency are compared and analyzed, if local abnormity exists, secondary compensation spraying parameters are generated, and fine correction is completed. The spraying continuity can be effectively improved, the quality risk caused by interruption is reduced, and workpiece waves are avoided.

The invention discloses a data acquisition-based dynamic path planning method and system for an intelligent industrial robot with a body, and relates to the technical field of industrial robots. The method comprises the core steps of system calibration and parameter presetting before machining, multi-mode sensor dynamic acquisition, point cloud preprocessing, curvature sensing dynamic ICP registration, micro-inertia disturbance compensation, dynamic path planning, procedure adaptation adjustment, real-time feedback correction and the like. The point cloud quality is guaranteed through multi-sensor cooperation and dynamic switching, the registration effect is adjusted and optimized through curvature correlation parameters, dynamic compensation is achieved through a disturbance prediction model, the connection efficiency is improved through procedure parameter mapping, and accurate fitting of paths is guaranteed through graded feedback correction. The method is suitable for machining complex curved surfaces such as artificial joints, machining precision and stability can be remarkably improved, and the method has extremely high scene migration capacity, can provide reliable technical support for precision machining of similar complex curved surfaces and has remarkable popularization value.

The invention discloses an automatic driving carrying equipment scheduling system and method for an industrial robot. The system comprises a plurality of automatic driving carrying devices, a central dispatching device and corresponding communication modules. The system adopts a multi-agent reinforcement learning framework and combines a graph neural network processing environment topological structure to realize dynamic path planning and multi-device collaborative scheduling; integrating an energy consumption prediction mechanism based on a Kalman filter, and bringing energy consumption factors into a task allocation decision; meanwhile, a fault-tolerant management mechanism based on a distributed account book and federated learning is established, and fault detection and rapid recovery are achieved. The technical modules are deeply coupled, and a unified collaborative optimization framework is formed through reward function design, utility function optimization and fault probability calculation of multi-agent reinforcement learning. According to the method, the problems of poor dynamic environment adaptability, isolated decision making of each module, extensive energy consumption management and the like in the prior art are effectively solved, and the overall efficiency, energy efficiency and reliability of a scheduling system are remarkably improved.

The application relates to the technical field of cable detection and belongs to a detection device for testing the pressure resistance of a cable. The device comprises a detection table, a fixed support connected to the detection table, a sliding support connected to the fixed support in a sliding mode, a hydraulic push rod connected to the fixed support, a pressure block connected to the extension end of the hydraulic push rod, the pressure block being located above the fixed support, a missing tooth ring arranged on the detection table, two connecting plates symmetrically connected to the missing tooth ring, and a swing support connected to the two connecting plates in a rotating mode. The swing support swings in two directions, and the pressure block extrudes the cable, so that the state of the cable under the combined mechanical stress is simulated. The test results of the cable under the pressure test in the state can reflect the insulation performance evolution of the cable under the dynamic mechanical stress, and the new cable applied to the joint of an industrial robot can provide reference value and reliable data.

The invention discloses an industrial robot assembly workstation, and belongs to the field of industrial robots. The industrial robot assembly workstation comprises a workbench, mechanical arms are installed on the two sides of the top of the workbench, and switching parts are installed at the ends of the two sets of mechanical arms; the number of the bearing tables is two, the bearing tables are fixed to the two sides of the workbench correspondingly, multiple sets of adsorption parts with different specifications are installed on the bearing table on one side, and cleaning parts with different specifications are installed on the bearing table on the other side; through cooperation of the two sets of mechanical arms and the adsorption pipes 43, different adsorption discs and dust suction pipes can be rapidly replaced according to the specifications of workpieces to be assembled, the applicability of the equipment is improved, and the assembling efficiency is ensured; and meanwhile, the surface of the workpiece can be comprehensively adsorbed and cleaned, the cleanliness of the surface of the workpiece is improved, the adverse effect of dust and impurities attached to the workpiece on assembly is reduced, and the assembly precision is ensured.

The invention belongs to the technical field of information, and particularly relates to an industrial robot semiconductor etching system based on intelligent regulation and control. Based on an etching precision control algorithm of multi-sensor fusion and real-time feedback, a Dempster-Shafer evidence theory is used for fusing multi-sensor data, and etching parameters are adjusted in real time in combination with sliding mode control, so that the chip etching size deviation is reduced to + / -1nm from + / -5nm of a traditional process, and the rejection rate is reduced to 5% from 15%. According to an etching uniformity optimization algorithm based on multi-physical field monitoring and adaptive adjustment, a multi-physical field model is constructed through multi-source information fusion, process parameters are optimized in real time by adopting model prediction control, the etching depth deviation is reduced to + / -5nm from + / -15nm, the chip yield is improved to 90% from 70%, and the etching precision and uniformity are remarkably improved.

The invention relates to the field of mechanical transmission control, and discloses a self-adaptive torsion optimization method and system based on a stress model. The method mainly comprises the following steps: acquiring strain, temperature and vibration data of a transmission system in real time through a distributed sensor network; constructing a three-dimensional dynamic stress field model, and marking a high-risk area; dynamically adjusting a safety threshold based on material fatigue characteristics, and generating a torque control curve by adopting an optimization algorithm; and eliminating response delay through a pre-compensation mechanism. The system comprises an edge calculation unit, a millisecond-level actuator and a grading protection module. According to the method, the tooth root stress concentration is successfully inhibited by 35% in the wind power gear box, the impact load is reduced by 52% in the industrial robot joint, the equipment reliability is remarkably improved, and the service life is remarkably prolonged. The device is suitable for transmission systems of wind power, electric automobiles, industrial robots and the like.

The invention relates to the field of industrial robot control, in particular to a trajectory planning method for a self-adaptive vibration suppression mechanical arm. According to the method, a mechanical arm motion vibration signal is monitored in real time through a vibration sensor, and original data are preprocessed to obtain a cleaning vibration signal; calculating vibration characteristic parameters including frequency, amplitude and phase; based on a reinforcement learning algorithm, a reference trajectory is dynamically adjusted through a strategy network according to the vibration characteristic parameters to generate a vibration suppression trajectory, and a reward function takes the vibration parameters as input optimization network parameters; and finally, the vibration suppression track is converted into a joint movement instruction to drive the mechanical arm, and closed-loop control is formed. According to the self-adaptive vibration suppression method, self-adaptive vibration suppression independent of an accurate model is achieved, and the motion precision and stability of the mechanical arm under different working conditions are improved.

The invention discloses an industrial robot grabbing and positioning system based on computer vision, and relates to the technical field of grabbing and positioning, and the system comprises a projection calculation module which is used for determining a roller direction unit vector of a roller based on a calibration plate image, and constructing a projection data pair based on the calibration plate image and the roller direction unit vector; the data acquisition module is used for generating a one-dimensional cosine curve based on the projection data pair and the candidate angular frequency set of the protection window; the image correction module is used for performing coordinate correction on the pixel coordinates of the calibration plate image based on the one-dimensional cosine curve to obtain a corrected image; the key point extraction module is used for carrying out geometric feature extraction on the correction image to obtain a key point set; and the pose optimization module is used for determining the optimal pose of the calibration plate relative to the camera based on the internal reference matrix of the camera and the three-dimensional reference point set and the key point set of the calibration plate. The grabbing stability and reliability of the robot are improved.

The invention relates to the technical field of industrial robots, in particular to a dynamic simulation damping parameter solving method based on a gravity compensation mechanism, which comprises the following steps of: acquiring a joint basic parameter set of a to-be-tested industrial robot, and determining a test boundary condition according to the joint basic parameter set, and constructing a test task sequence based on the test boundary conditions. According to the method, the fusion weight of the static stiffness and the dynamic stiffness is intelligently adjusted according to the characteristics of the movement speed, the acceleration or the force control frequency band in a specific application task, and the limitation of a single parameter under complex and changeable working conditions is avoided. According to the parameter fusion mechanism aiming at the specific task characteristics, the generated joint comprehensive rigidity parameters can truly reflect the physical response of the robot in different operation stages, and the prediction error between the physical simulation environment and the actual physical operation is reduced; and the strict requirements of high-precision scenes such as semiconductor packaging and the like on micron-level positioning precision and force control stability are met.

The invention discloses a multi-degree-of-freedom clamping mechanism of an industrial robot. The multi-degree-of-freedom clamping mechanism comprises a control base, a shaft base set and a clamping assembly. A first driving motor and a second driving motor are mounted on the bottom surface of the control seat; the shaft seat group is rotationally mounted on the inner side of the control seat and consists of a ring seat, a first transmission gear, a second transmission gear, a roll shaft and a shaft rod, the first transmission gear is meshed with the first driving motor to realize integral deflection, and the second transmission gear is meshed with the roll shaft through a bevel gear to realize rotary transmission; the clamping assembly comprises a swing seat, a clamping seat and a clamping lug, and a crank gear is driven by a shaft rod and a worm to drive the clamping lug to be opened and closed. And a touch switch is arranged on the guide cover and is matched with the servo control module to realize limiting protection and automatic resetting. The multi-posture clamping mechanism is compact in structure, high in degree of freedom and stable in transmission, multi-posture clamping and intelligent safety control of workpieces can be achieved, and the operation flexibility and safety of the industrial robot are remarkably improved.

The invention provides a robot hand-eye online calibration method based on a self-supervised consistency optimization algorithm, and the method comprises the steps: employing the posture change of a mechanical arm collected in a production task, enabling a camera to observe the posture of a workpiece and an actual error after grabbing / assembling, and building an initial hand-eye relation through combination with one-time offline calibration; the eye pose adjustment amount of the player is calculated in a segmented mode under the incremental geometric framework; the self-supervised network outputs observation reliability and an offset type from time sequence features, carries out screening and reconstruction to generate a long-term hand-eye pose adjustment amount and a single-task pose compensation amount which are respectively used for updating a hand-eye relationship and correcting a control instruction, and carries out self-supervised updating on the network based on historical records; according to the method, separation treatment of slow deviation and short-time disturbance of the structure is achieved, manual re-calibration is reduced, the positioning precision and stability of visual guidance grabbing / assembling are improved, and the method is suitable for industrial robots running on line.

A method and system (200) for determining an elastic characteristic of an industrial robot (100) including a plurality of interconnecting shafts including at least one shaft exhibiting elasticity and at least one joint exhibiting static friction. The method comprises: (S12) clamping an end flange (16) of the industrial robot (100) to the flexible clamping device (1); (S15) determining and loading a program into a robot controller of the industrial robot (100), the program defining a clamping deflection motion that produces deflection of one or more links of the industrial robot (100) based on a force interaction between the robot (100) and the flexible clamping device (1); (S16) controlling the industrial robot (100) to perform a clamping deflection motion while sensing a quantity of the industrial robot (100) and a quantity of the flexible clamping device (1); (S17) determining a reduced data set of the sensing measurements of the industrial robot (100) and the flexible clamping device (1) by excluding data of the sensing measurements that may comprise the influence of static friction; (S18) determining the relative attitude and force / torque between the top plate (2) and the base plate (3) of the flexible clamping device (1); and (S19) determining kinetic friction characteristics of the at least one joint exhibiting static friction and elastic characteristics of the at least one shaft exhibiting elasticity, and (S21) updating one or more of the at least one model of the industrial robot (100) with the determined kinetic friction characteristics and elastic characteristics, wherein the updated one or more in the at least one model provides elastic characteristics of the industrial robot (100) and related dynamics forming a scenario for using the parameters to improve control of the industrial robot (100).

The invention relates to an intelligent control method, system and device for an industrial robot and a medium. The method comprises the steps that high-dimensional coding is conducted on the environment and the body state of the robot to generate a unified hidden state vector; generating a task-driven original control instruction through a deep reinforcement learning network based on the hidden state vector, predicting the safety risk of the instruction through a safety network in a prospective manner, and outputting safety measurement and gradient data; when the risk is predicted to exist, constructing and solving an optimization model taking minimization intervention as a target, and calculating to obtain a safety correction value; fusing the correction value and the original instruction to generate a final safety control instruction and executing the final safety control instruction; and updating the security prediction network online by using the executed real security feedback. The method solves the problems that in existing deep learning control, the decision risk is unpredictable in physical deployment, and a traditional safety protection mechanism extensively interrupts tasks, and the intelligent decision-making ability of the robot is reserved to the maximum extent while absolute safety is guaranteed.