745 results about "Collaborative robotics" patented technology

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

Provided is a self-adaptive identification method for nonlinear dynamic parameters of a reducer, which belongs to the design field of a reducer. The method includes: modeling a harmonic reducer corresponding to a flexible joint as a concatemer of a rigid reducer and an elastic torsion spring, and carrying out dynamic theoretical modeling and parameter variable independence processing on the concatemer to form a dynamic equation for parameter identification; giving an optimized motion trajectory to each joint of a robot and controlling the robot to act accordingly, acquiring relevant data needed for parameter identification based on a built-in torque sensor and double encoders inside the joint; using an offline identification algorithm to accurately identify a plurality of dynamic parameters of a collaborative robot considering joint flexibility and friction, and obtaining a minimum parameter set.

The invention discloses an automatic stacking control system based on visual positioning. The automatic stacking control system comprises a multi-mode visual positioning module, a model building module, a dynamic path planning module, a mechanical arm execution module, an error correction module and a storage module. The multi-mode visual positioning module comprises an RGB camera, a sensor and a laser radar and is used for obtaining three-dimensional space information of goods and a stacking area in real time. The multi-modal visual positioning module is mounted on one side of the stacking area; the model building module is used for building a stacking area, the cargo position and the position of the mechanical arm in a three-dimensional space, when the system is used, information about the position of the stacking area and information about whether new cargoes can be stacked in the stacking area or not can be obtained, then movement at any position in the space can be achieved through the three-axis cooperation mechanical arm, and the three-axis cooperation mechanical arm can move at any position in the space. The application range of the system is widened, and cargoes can be accurately conveyed to the position of a stacking area through the dynamic path planning module and the error correction module.

New and innovative systems and methods for calibrating and correcting sensors associated with a collaborative robot are disclosed. An example system comprises: at least one robotic arm; a sensor affixed to a location on the robotic arm, wherein the sensor measures force and torque across six degrees of freedom (6DOF); a processor; and memory. The system may receive, from the sensor, sensor input in real-time that indicate a measured force or torque. The system may generate, in real-time, sensor corrections that correspond to offset, linear, and non-linear deviations of the measured force in each sensor axis. The sensor corrections may correspond to offset, linear, and non-linear cross-coupling of the measured force between two or more sensor axes. The sensor corrections may be determined by applying offset, linear, and non nonlinear corrections to each degree of freedom (DOF) from every other DOF.

The invention discloses a satellite flash connection intelligent switching decision-making system and method based on equipment state prediction, and particularly relates to the technical field of satellite flash connection. In order to solve the problem of star-flash network linkage failure caused by multi-mechanical-arm cooperative movement and electromagnetic disturbance coupling in an industrial cooperative robot system, a prediction model fusing space attitude cooperative change characteristics and transient electromagnetic disturbance amplitude gradient characteristics is constructed, and high-sensitivity recognition and risk early warning of the coupling state of a star-flash connection network are achieved. A communication channel and communication parameters are dynamically adjusted through a fuzzy logic decision mechanism, and link stability and interference robustness are effectively improved; and meanwhile, a model adaptive optimization mechanism driven by switching effect feedback is introduced, so that the prediction accuracy and the system self-evolution capability are remarkably improved, and the continuity, the safety and the high reliability of multi-node cooperation in an industrial communication scene are guaranteed.

The invention relates to the technical field of industrial Internet of Things, in particular to an industrial Internet of Things system for collaborative robot monitoring, which comprises the following steps: a sensor calibration module, a running state modeling module, a working performance analysis module, an input parameter analysis module, a movement track detection module and a fault type analysis module. According to the method, the accuracy and reliability of collected data are ensured by calibrating the reading of the sensor, the accuracy of robot state evaluation is optimized, the ideal motion trail of the robot is predicted in real time, the actual position is monitored, the position deviation and the abnormal trail are recognized, and the accuracy of robot state evaluation is improved by combining the calibration of abnormal points and the matching of fault modes. Fault types are quickly identified, fault positions are positioned, fault detection efficiency and accuracy are improved, adaptability of the robot in a complex production environment is improved, operation efficiency and maintenance precision of the robot are enhanced, downtime is shortened, maintenance cost is reduced, and accurate production monitoring and decision support are provided for enterprises.

The invention discloses a flexible composite robot system for laboratory automation. The flexible composite robot system comprises a flexible composite robot and a central scheduling and control system, the flexible composite robot comprises an autonomous mobile platform which carries a multi-mode navigation sensor and realizes path planning and obstacle avoidance in a laboratory environment by adopting a synchronous positioning and mapping algorithm; the high-precision cooperative mechanical arm is mounted on the autonomous mobile platform and is integrated with a joint torque sensor; the end effector quick replacement system provides mechanical, electrical and pneumatic standardized interfaces; the central scheduling and control system comprises an intelligent man-machine interaction system for realizing man-machine interaction; the experiment process analyzer is used for decomposing the digital experiment scheme into an executable task sequence; the multi-agent task planning and scheduling engine is used for distributing tasks and planning a moving path; and the anomaly detection and autonomous recovery module is used for detecting an abnormal condition based on machine vision and force sensing data and triggering a recovery strategy.

The invention relates to the field of robot intelligent control and man-machine collaboration, in particular to a multi-sensing collaborative robot control method, which comprises the following steps: synchronously filtering mechanical, tactile, visual and physiological signals to generate a unified observation frame, and calculating power flow and energy in combination with a joint state to form an execution vector; extracting a topological descriptor by using persistent coherence, and outputting an interaction potential field and a human body intention by using the topological descriptor as a conditional driving diffusion model; then, power density is constructed based on the potential field gradient and intention, joint torque and impedance are generated through entropy regular optimal transmission and Jacobian and microoperator kernel mapping, and an energy Lagrange multiplier is updated in real time; and if the consistency error continuously exceeds a threshold value, estimating a Koopman lifting model on line by using a causal weight, completing model prediction control under the energy, conformal security and topology consistency constraint, outputting an update instruction and performing closed-loop feedback. The method improves the cooperation precision and safety, reduces the energy consumption, and is suitable for a high-dynamic man-machine cooperation scene.

The invention relates to the technical field of two-arm robot pipeline butt joint cooperative motion planning and control in a complex environment, in particular to a two-arm cooperative robot pipeline butt joint method based on relative Jacobi. According to the method, a double-arm kinematics equation relative to a Jacobian matrix is constructed, constraint conditions are constructed in combination with pipeline butt joint requirements, pose errors and integral information are introduced to design an anti-noise motion planning scheme, an end effector obstacle avoidance strategy is designed, the anti-noise scheme is converted into a quadratic optimization problem, and the quadratic optimization problem is solved through a numerical algorithm. According to the method, the problems that an existing scheme is insufficient in precision, inaccurate in constraint condition and poor in anti-noise and obstacle avoidance capacity are solved, the uniformity and accuracy of butt joint of the two-arm pipelines are improved, the influence of environmental noise is effectively restrained, butt joint safety is guaranteed, butt joint efficiency and reliability are improved, and the high-precision pipeline butt joint requirement under the complex environment is met.

The present invention relates to a collaborative robot system used in an industrial environment where workers collaborate. The collaborative robot system comprises: a collaborative robot body having a multi-joint structure; a control unit for controlling each joint or auxiliary shaft; one or more sensors for detecting the operational state of the robot in real time; and a status diagnosis and response module for diagnosing the state of the robot on the basis of detected data and controlling the operation of the robot accordingly. Specifically, the system is configured to enable the robot to autonomously perform actions such as deceleration, stopping, and recovery in response to various state changes occurring during work, and to perform self-learning and trajectory optimization on the basis of data accumulated through repetitive tasks. Furthermore, the system includes a graphical user interface (GUI) for intuitive user interaction, which is linked to a digital twin-based virtual simulation environment, enabling presetting and modification of work paths. When multiple collaborative robots are operated together, the system enables task synchronization, path collision avoidance, and sharing of status information among the robots, and may be connected to an external control server or a cloud-based control system to allow integrated management of the entire workflow.

The invention discloses a constraint following control method of a two-arm collaborative robot based on differential homeomorphic mapping, and relates to the field of robot control. According to the method, a nominal dynamical model of a robot is established, and a geometric constraint equation is defined according to a cooperative task; establishing global differential homeomorphic mapping by constructing constraint tracking variables and complementary coordinates, and decoupling a system dynamics model into a constraint tracking subsystem and a complementary subsystem; designing a robust adaptive controller fusing sliding mode control and an adaptive law, and estimating and compensating the uncertainty of the system on line; according to the method, the constraint processing complexity is effectively simplified through differential homeomorphic mapping, the constraint tracking precision and robustness of the system under model uncertainty and external interference are remarkably improved, and high-precision and high-stability cooperative task execution of the two-arm robot in the dynamic environment is achieved.

The invention discloses a rapid auxiliary assembly collaborative robot suitable for an aircraft cabin door, and belongs to the technical field of aircraft assembly, the rapid auxiliary assembly collaborative robot comprises a main body structure, an adsorption unit, a power system and a control system, the main body structure 1 carries the adsorption unit, the power system and the control system, and the main body structure 1 is matched with the power system and the control system to realize multi-pose follow-up control; the power system provides pneumatic power for the adsorption unit to adsorb cabin door products. The power system provides negative pressure for work of the adsorption unit and provides power for follow-up control of the main body structure. The control system controls adsorption, release and multi-degree-of-freedom free posture adjustment. The device has high degree of freedom, adapts to aircraft cabin doors with different shape and position requirements, can be accurately operated in a narrow assembly space, is flexible in structure, simple and convenient to operate, and rapid and accurate to move and position, greatly shortens the time of loading, unloading and hoisting of the cabin door, and shortens the cabin door assembly time by about 70%.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention belongs to the technical field of intelligent and robot operation, and discloses a collaborative robot closed-loop operation method based on scene perception and thinking chain reasoning, which comprises the following steps of: firstly, generating structured semantic representation containing confidence by utilizing a visual language model of LoRA efficient fine tuning and fusing multi-view data, and providing semantic understanding for a robot; secondly, utilizing a three-dimensional diffusion model to generate explicit geometric priori under the condition of no real CAD, and correspondingly completing PnP initial pose estimation in combination with 2D-3D; then, carrying out pose optimization by adopting a tracking-refining strategy, and triggering reinitialization based on geometric prior when a residual error exceeds a threshold value; and finally, decomposing a global task by utilizing a thinking chain reasoning module, and realizing'perception-reasoning-execution 'closed-loop control based on visual servo in cooperation with an execution module. The method realizes effective combination of large model reasoning and physical execution, has strong robustness under dynamic disturbance, and is suitable for intelligent manufacturing and man-machine cooperation scenes.

The invention discloses an in-situ remanufacturing repair system and method based on three-dimensional point cloud.The in-situ remanufacturing repair system comprises an industrial personal computer, a binocular structure light camera and a collaborative robot, the binocular structure light camera and the collaborative robot are connected with the industrial personal computer, and the collaborative robot is provided with a welding gun connected with an intelligent welding machine; the method comprises the following steps of: performing hand-eye calibration on a binocular structured light camera by adopting dual quaternion and a singular value decomposition algorithm; a binocular structured light camera is used for shooting the workpiece, and a complete point cloud of the to-be-repaired area is obtained; performing surface reconstruction on the to-be-repaired area by using a Poisson surface reconstruction algorithm; according to the side wall fusion optimization formula, path planning is conducted on the to-be-repaired area model in Cura software, and filling path coordinates of the to-be-repaired area are obtained. According to the method provided by the invention, the defect of incomplete fusion of the side wall of the workpiece after additive remanufacturing repair can be reduced, and meanwhile, the surface flatness of the weld joint adjacent to the side wall is improved.

The invention discloses a collaborative robot joint external force estimation method, and relates to the technical field of collaborative robot joint external force estimation, and the method comprises the steps: building a joint kinetic model, and obtaining the basic kinetic parameters of a collaborative robot joint; the angular displacement deviation between the input shaft and the output shaft of the speed reducer is obtained, and the compensated torsional angle difference is calculated according to the angular displacement deviation; a joint variable stiffness model is established, multiple sets of calibration data are obtained through experiments, and parameters in the joint variable stiffness model are identified according to the multiple sets of calibration data; based on the basic dynamic model and the joint variable stiffness model after parameter identification, a nonlinear disturbance observer is constructed in combination with introduced auxiliary variables; and system disturbance is observed and compensated through a nonlinear disturbance observer, so that an external torque estimated value acting on the tail end of the joint is calculated and output in real time. According to the method, high-precision and real-time estimation of the external torque can be effectively considered, and meanwhile, the system hardware cost and the structure complexity are greatly reduced.

The invention belongs to the technical field of robots, and discloses a collaborative robot intelligent feeding and discharging system, which comprises a station structure sensing module for collecting station structure information of a station where a collaborative robot is located when the collaborative robot executes feeding and discharging tasks, and performing structured coding on the station structure information to generate a station structure constraint matrix; the part task decomposition module is used for constructing a loading and unloading task queue of parts on the basis of the station structure constraint matrix, and converting the loading and unloading task queue into an action instruction set which can be executed by the collaborative robot in combination with a preset station rhythm requirement; the path deduction avoidance module projects the action instruction set to a joint space of the collaborative robot, and deduces and generates a candidate path set based on a station constraint path mapping mechanism; performing optimization selection on the candidate paths by adopting dual-target scheduling of energy consumption and process rhythm, and selecting the candidate path with the optimal performance as an execution path of the robot; and the safety of feeding and discharging operation of the collaborative robot is improved.

The invention discloses a cooperative robot joint adaptive control system and method based on multi-sensor fusion, and relates to the technical field of robot joint control. According to the invention, through an improved extended Kalman filtering and deep neural network compensation mechanism integrated by the multi-source data fusion unit, sensor noise is effectively suppressed, and multi-source data conflicts are solved; the sensor reliability evaluation unit is based on a dynamic weight distribution mechanism of Bayesian reasoning, and can reduce the fusion weight of the sensor in real time when the sensor is detected to be abnormal, and improve the contribution degree of reliable sensor data. The adaptive control unit is combined with the self-learning optimization unit to form a closed-loop optimization architecture, so that the performance limitation caused by fixed traditional PID parameters is thoroughly solved; through adaptive weighted fusion of multi-source sensor data, the joint state estimation precision is improved; and a deep neural network residual error compensation mechanism is utilized to reduce a torque control steady-state error.

Robot calibration is crucial in multi-robot cooperative systems where the inaccuracy of robots can add up and cause large errors in the final trajectory of handled parts or process tools. In this work, a two-step calibration approach is proposed based on artificial neural networks (ANNs) and definition of compensated pose for a master-slave cooperative robot system. Measuring the pose of master and slave robots at different locations in their shared workspace is required to create pairs of joint angles and output pose errors as training data. The generated data is used to train two ANN models for compensating the master-slave relative error and the master robot errors. The master-slave relative error is corrected by introducing a compensated pose for the slave robot with respect to the master robot. A neural network is then trained to predict the error parameters of the compensated pose for the joint angles of both robots as the input. The master robot is then corrected individually using another ANN model to address the absolute accuracy of the cooperative system.Measurements and simulations have been performed on a dual-robot cooperative system before and after geometric calibration. The process of cross validation is carried out to find the best network architecture for the optimal performance in correcting the robots'errors. It has been shown that even after pre-existing model-based calibration of each robot, both the absolute accuracy of the master robot and the relative tracking accuracy can be further improved by the proposed implementation of ANN calibration.

The invention relates to the technical field of robot vision, in particular to a three-dimensional reconstruction viewpoint planning method based on collaborative robot 3D vision, which comprises the following steps: S1, performing visual calibration on a 3D camera on a collaborative robot; s2, whether a workpiece standard model exists or not is judged, if yes, a candidate viewpoint set is constructed according to the workpiece standard model, and if not, the workpiece standard model is established according to preset fixed multiple viewpoints, and then the candidate viewpoint set is constructed; and S3, a deep reinforcement learning mechanism obtains a planning result according to the candidate viewpoint set, controls an actual six-axis collaborative robot to move according to the planning result, carries out data acquisition, obtains 3D point cloud data of the workpiece at the visual angle, carries out preprocessing including noise point removal and filtering, and generates a three-dimensional model of the workpiece based on the fused point cloud data. The three-dimensional reconstruction method is high in automation degree, almost does not need manual operation in the whole process, and can effectively perform efficient and rapid three-dimensional reconstruction on a large batch of different types of non-standard customized small-batch workpieces.

The invention discloses a collaborative robot multi-mode sensing system based on digital control, and belongs to the technical field of collaborative robot control. According to the invention, sensing of various modal information of RGB images, depth images, pressure sensors and the like can be realized; the maximum distance between any two points on the outer contour line of the top surface of the workpiece is used as the size characteristic parameter of the workpiece, so that the pneumatic clamping jaw at the tail end of the workpiece grabbing robot can be effectively ensured to finish the grabbing task of the current work; the contour detection technology is combined with the depth camera principle, workpiece distance information can be calculated ingeniously and accurately, the workpiece grabbing robot can be controlled to drive the pneumatic clamping jaw to descend by the corresponding distance, and it is guaranteed that follow-up grabbing work is conducted stably; the pressure sensor sets are arranged in the corresponding areas of the two clamping plates of the pneumatic clamping jaw, pre-clamping operation is combined, workpiece deflection angle information can be accurately recognized, and an accurate basis is provided for follow-up angle adjustment of the pneumatic clamping jaw.

The invention relates to a compact heavy-load cycloid oscillating tooth joint module which comprises a frameless torque motor, a joint shell, an eccentric shaft, a reverse cycloid wheel, a forward shock wave wheel, a middle connecting shaft, an epicycloid gear transmission system and a hypocycloid oscillating tooth transmission system. The frameless torque motor is installed in the joint shell, the output end of the frameless torque motor transmits torque to the eccentric shaft, the eccentric shaft drives the reverse cycloidal gear to rotate, and the reverse cycloidal gear and the forward shock wave wheel are connected through a middle connecting shaft and rotate in the same direction. Torque generated by the epicycloid gear transmission system is transmitted to the hypocycloid oscillating tooth transmission system, and the hypocycloid oscillating tooth transmission system outputs torque after speed reduction. The invention has the advantages of high precision, high rigidity, high bearing capacity, small volume, small back clearance and the like, can be used for application scenes with small installation space and heavy load requirements, and is particularly suitable for a small-volume heavy-load joint module of a collaborative robot.

The invention relates to the field of collaborative robot control, in particular to an active compliant interaction control method and system for a collaborative robot without a force sensor. According to the method, the man-machine interaction force is estimated in real time through the interaction force estimation system, additional sensors are not needed, and the hardware cost is reduced; solving an optimal impedance model through strategy iteration by using an impedance model optimization system to adapt to an uncertain interaction environment; and the reference trajectory of the robot is corrected through the active compliant interaction control system, so that accurate and reliable compliant control is realized. According to the main scheme, the method comprises the steps of interaction force estimation based on generalized momentum, target impedance equation modeling, performance index function optimization and barrier Lyapunov function control input design. The method solves the problems that a traditional collaborative robot is high in hardware cost, poor in interaction adaptability, unstable in control and the like, and is suitable for man-machine interaction scenes in the fields of intelligent manufacturing, medical health and the like.

The invention relates to a cooperative robot control method based on a self-adaptive observer and a neural network. The cooperative robot control method comprises the steps of system modeling and signal collection, operator identity recognition and feature extraction, super-spiral disturbance observer, frequency domain self-adaptive admittance filter and composite control law synthesis. A kinetic model is constructed and disturbance terms are defined by collecting man-machine interaction torque and joint states in real time; extracting the characteristics of an operator, carrying out identity recognition by adopting an incremental DBSCAN algorithm, and associating personalized admittance parameters; designing a super-spiral disturbance observer to dynamically estimate disturbance; adaptively adjusting admittance parameters according to the identity of the operator and the frequency band characteristics to generate an expected trajectory; and the sliding mode control and the double-loop neural network compensation item are combined to synthesize the control torque, so that high-precision and smooth man-machine interaction control is realized. The method has the advantages of being high in anti-interference performance, good in personalized adaptability, high in tracking precision and the like.

A collaborative robot including: a movable base to movably position the collaborative robot at different variable work locations; an articulated robot actuator movable relative to the base to effect with a robot end effector a predetermined function corresponding to at least one workstation at the at least one work location, the at least one workstation having an undeterministic variable pose with respect to the at least one work location; a vision system connected to the articulated robot actuator to image a vision target connected to and corresponding uniquely to each of the at least one workstation; and a controller to determine from the image data the workstation pose relative to the movable base and automatically teach the articulated robot actuator the workstation pose so as to effect a predetermined deterministic interface, associated with a predetermined workstation function characteristic, between the workstation and robot end effector.

The invention relates to a graphical programming compiling system, method and device for a collaborative mechanical arm. The system comprises a user terminal, a compiler, an actuator and a collaborative mechanical arm, the user terminal responds to code writing operation of a user on a human-computer interaction interface and / or dragging operation of graphic elements, program data is generated, and the program data is issued to the compiler; the compiler converts the program data into a cooperative mechanical arm control instruction, an external equipment control instruction and a logic control instruction; the executor executes the logic control instruction and sends the cooperative mechanical arm control instruction and the external equipment control instruction to the cooperative mechanical arm and the external equipment respectively; the mechanical arm and the external equipment are cooperated to execute matched tasks, and first task feedback information and second task feedback information are generated and fed back to the actuator; and the executor generates an execution result according to the feedback information and pushes the execution result to the user terminal. By adopting the method, the use difficulty of the cooperative mechanical arm is reduced.

A method of programming multiple weld passes in a collaborative robot welding system to perform multi-pass welding is provided. A root pass is programmed for a first weld seam by manually positioning a welding torch and automatically recording root pass position and angle data. Secondary passes for the first weld seam are also programmed. The tip of the welding torch is positioned at a start point and a stop point for each secondary pass. The start and stop position data of the start point and the stop point are automatically recorded for each secondary pass. Numerical position and angle offset data are automatically calculated. The root pass position and angle data and the offset data are stored as a multi-pass template. The template is translated and applied to a weld reference frame of a second weld seam to aid in programming secondary passes for the second weld seam.

The invention discloses a joint vibration suppression method, device and equipment of a collaborative robot and a storage medium. The method comprises the steps that according to position information of a motor end encoder and position information of a harmonic reducer end encoder, an observation speed and speed observation delay containing vibration information are obtained; subtracting the observation speed from the given speed to obtain a speed deviation; obtaining a group of control parameters of the current working condition according to the load inertia and the tail end speed of the robot through an automatic variable gain strategy; performing speed loop PID control according to the speed deviation and the control parameters to obtain a control torque; load disturbance observation is carried out according to feedback information of the torque sensor and the observation speed, and harmonic reducer end disturbance torque is obtained; and adding the disturbance torque to the control torque to obtain the given torque of the torque ring. According to the invention, the speed loop feedback channel contains information which directly reflects the vibration of the robot, and the method can adapt to vibration suppression of various models in different postures.

The invention discloses a teaching gantry platform detachable manipulator with touch and force feedback and a control method, the teaching gantry platform detachable manipulator comprises a gantry platform, a teaching cooperation mechanical arm is mounted on one side of the bottom of the gantry platform, and a follow-up mechanical arm is mounted on the other side of the bottom of the gantry platform; the tail ends of the teaching cooperation mechanical arm and the follow-up mechanical arm are provided with a first fixing seat, and one side of the outer wall of the first fixing seat is provided with a welding gun. The invention relates to the technical field of welding equipment, contact force between the welding gun and a workpiece is sensed in real time at the tail end of the welding gun through a sensing assembly, a student can conveniently, accurately and rapidly adjust the relative position and angle of the welding gun at a welding track point according to actual welding requirements, meanwhile, a control system generates a welding track teaching program, and the teaching effect is improved. Synchronous operation of two or more welding manipulators is achieved, and the quality and efficiency of welding operation are improved.

A robotic system to facilitate simultaneous human laborer and robotic tasks on an article. The system includes data acquisition from a non-point cloud camera and implementation by a mid-tier consumer grade workstation. Nevertheless, a motion plan may be carried out by the robotic aid in a manner that allows for “on-the-fly” adjustment to a second motion plan to avoid collision with the laborer during the performed tasks.