1016 results about "Robot control system" patented technology

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention belongs to the technical field of automation control, and discloses an inspection robot control system and method based on inspection information, and the inspection robot control system based on the inspection information comprises a path planning module, an obstacle detection module, a region evaluation module, a path reconstruction module and a state recovery module. By acquiring the coordinate point set of the inspection map and the start-stop position of the robot and analyzing the path steering change and distance accumulation, the navigation precision of the inspection robot in a complex environment is optimized, roundabout and repeated movement caused by inaccurate path planning is reduced, the real-time distance between an obstacle and the path is measured and judged, and the navigation precision of the inspection robot is improved. And the response speed and accuracy of dealing with sudden obstacles are improved, when the path is reconstructed, through analysis of connection curve data, the smoothness of the path and the movement continuity of the inspection robot are ensured, the operation efficiency of the inspection robot is improved, the manpower monitoring demand is reduced, and the resource configuration is optimized.

The embodiment of the invention provides an unhooking robot control method based on multi-source visual fusion, which is applied to the technical field of robot control and comprises the following steps: acquiring an RGB image, a depth image, an infrared image and IMU data through a multi-source sensing system mounted at the tail end of a robot; carrying out feature fusion identification by adopting a double-branch neural network, and outputting the boundary contour of the lifting hook and the three-dimensional coordinates of the optimal grabbing point; the visual coordinates are unified to a robot base coordinate system through a registration correction mechanism; a Transform prediction model is constructed based on the visual and inertial signals, and future pose changes of the lifting hook are estimated; a feedforward control track is generated to counteract swing of the lifting hook, and track correction is carried out in combination with visual servo feedback; and a joint instruction is generated through path planning and inverse kinematics solution, and the mechanical arm is driven to complete precise unhooking operation. According to the method, the recognition precision, the anti-interference capability and the operation success rate of unhooking operation in complex illumination and dynamic environments are effectively improved.

The invention relates to the field of robot control, and provides a humanoid robot control system and method based on reinforcement learning, and the humanoid robot control system comprises a first control subsystem and a second control subsystem. The first control subsystem comprises a strategy reasoning module, a state conversion module and a robot control module; the second control subsystem comprises a data acquisition module and a driving control module; the data acquisition module is used for finishing timestamp alignment and abnormal value filtering of sensor data and transmitting the data to the state conversion module; the state conversion module is used for fusing multi-source sensor data and constructing a time sequence state feature containing a real-time measurement value and historical time sequence information; the strategy reasoning module is used for generating a multi-joint angle target value of the robot according to the time sequence state characteristics provided by the state conversion module; and the robot control module is used for analyzing the multi-joint angle target value output by the strategy reasoning module, selecting a control mode and generating a control command comprising a parameter adjustment instruction.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The invention discloses a lineation robot control system based on a satellite positioning and orientation technology, which relates to the technical field of robot motion control and comprises a satellite positioning and orientation module, an inertial navigation module, a fusion positioning and orientation software module, a motor control software module, a motion control software module and a pattern planning software module. The satellite positioning and orientation module is used for acquiring latitude and longitude coordinates of the current position of the robot by receiving multi-frequency satellite signals; the inertial navigation module is used for collecting posture and motion data of the robot in real time through a gyroscope and an accelerometer. According to the lineation robot control system provided by the invention, through collaborative design of the multi-frequency-point GNSS receiver and the anti-multipath interference antenna array, centimeter-level continuous positioning capability in a complex urban environment is realized, and satellite signal reflection and shielding interference in scenes such as viaducts and avenues are effectively overcome; and satellite positioning and inertial navigation data are deeply fused through a tight coupling Kalman filtering algorithm.

The invention discloses a self-adaptive memory enhancement imitation learning method for a robot control task, which comprises the following steps of: performing standardization processing and time sequence feature extraction on original multi-modal data, and constructing hierarchical feature representation; establishing a layered structure including short-term memory, working memory and long-term memory, and evaluating memory importance and dynamically updating memory content based on use frequency, task correlation and novelty; retrieving related memories according to the current state and carrying out multi-modal feature fusion; a shaping reward is calculated based on neighbor sample search; and optimizing strategy network parameters through a multi-loss function combination. Through adaptive memory management and a multi-modal feature fusion mechanism, the learning efficiency and execution performance of the robot control system are improved.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

The invention provides an intelligent welding robot control system, belongs to the field of artificial intelligence, and is suitable for welding process control in industrial automatic production. Through integration of a multi-modal data fusion technology, a complex deep learning algorithm and a real-time path optimization and parameter adaptive control technology, all-directional intelligent monitoring and optimization control of the welding process are realized. The method is particularly suitable for the intelligent production environment needing high-precision and high-efficiency welding operation, the automation level and the product quality of a production line can be remarkably improved, and the production efficiency and the competitiveness of the intelligent manufacturing field are improved.

The invention discloses a robot control system and method based on vision and force sense fusion and a robot, and relates to the technical field of robots. The robot control system based on vision and force sense fusion and the control method thereof analyze visual features in real time through an artificial intelligence processing unit to predict mechanical parameters; and the self-adaptive impedance control unit is combined to dynamically fuse a position instruction and force sense feedback, so that precise adaptation of target object characteristics and compliant regulation and control of an interaction process are realized, real-time sensing and dynamic adjustment capabilities are achieved, assembly damage caused by mechanical mismatching is effectively avoided, and the reliability and efficiency of precise assembly are improved.

The invention discloses a monitoring system and method for working data of an AGV robot, and belongs to the technical field of artificial intelligence. The method comprises the following steps: preprocessing and integrating the driving track, freight weight, running state and task execution data of the AGV robot to form a robot working data set; clustering analysis is carried out based on driving tracks of all robots, and similar tracks are clustered into one class; constructing a dynamic driving track planning model by using an A * algorithm, and generating a plurality of candidate optimal driving tracks for each task combination; constructing a task demand list, and carding a task sequence logic relationship; performing task allocation on the AGV robot by using a greedy algorithm; generating a task allocation list by using a simulated annealing mechanism; a task instruction is issued to a corresponding AGV robot control system, a task execution process is started, and whole-course tracking is started; and after the AGV robot completes a single task and a round of task sequence, a model self-learning updating mechanism is triggered.

The invention relates to the technical field of robot control, and discloses a heterogeneous robot control system with intelligent and multi-modal perceptual driving, comprising: a quantum clock reference module used for generating a globally synchronized picosecond-level time reference signal and synchronizing a multi-modal sensor clock through a microwave pulse; the multi-mode sensing module comprises an event camera of which the dynamic range is greater than or equal to 120dB, a six-dimensional force sensor of which the noise density is less than or equal to 0.01 N / Hz and an inertial measurement unit of which the zero-bias stability is less than or equal to 0.8 degree / h; and the random differential manifold module adopts a four-layer full-connection neural network, and an input layer receives multi-modal data. According to the invention, a diamond NV color center system of the quantum clock reference module and a microwave pulse coupling technical scheme are adopted, so that a picosecond-level global signal synchronization effect is achieved. Compared with a scheme depending on a traditional crystal oscillator or a GPS clock in the prior art, the defects that electromagnetic interference is prone to occurring and signal jitter is large are overcome, and high-precision time sequence alignment of multi-module cooperation is ensured.

The present disclosure provides a method for generating annotation data for robotic training using a hierarchical transformer-based model with multiple layers. The transformer-based model includes Alpha models generating low-level control outputs and Beta models generating high-level control outputs. The method receives multimodal input data comprising visual sensor data and natural language instructions, processes this data through the hierarchical transformer-based model to generate annotations at different abstraction levels, wherein Beta models create semantic annotations describing task objectives and Alpha models generate motor command annotations specifying robotic actions, and stores these annotations with the input data to create annotated training data for robotic control systems.

The invention discloses a multi-warehouse management warehouse-in and warehouse-out method and a digital warehouse management system, and the digital warehouse management system carries out the data interaction with an enterprise resource management system, a manufacturing execution system, a warehouse control system and a robot control system through an API interface. The method comprises the steps of monitoring data sent by a manufacturing execution system and a handheld terminal in real time, if a raw material warehousing order number sent by the handheld terminal is monitored, executing a raw material warehousing process, if a raw material ex-warehouse request of the manufacturing execution system is monitored, executing a raw material ex-warehouse process, and if a semi-finished product warehousing request of the manufacturing execution system is monitored, executing a semi-finished product ex-warehouse process; and if a semi-finished product warehouse-out request of the manufacturing execution system is monitored, executing a semi-finished product warehouse-in process, if a semi-finished product warehouse-out request of the manufacturing execution system is monitored, executing a semi-finished product warehouse-out process, and if carton data of finished product warehouse-in of the manufacturing execution system is monitored, executing a finished product warehouse-in process. According to the invention, a plane library, a three-dimensional library and a line-edge library are all included in a management range, and meanwhile, the dependence on manpower is reduced.

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 discloses a magnetic control arc GTAW-GMAW composite narrow gap welding system and method. The system comprises a GTAW welding gun, a GMAW welding gun, a magnetic conductive iron core, a magnet exciting coil, a magnetic boot, an assembling clamp, a GTAW welding power source, a GMAW welding power source and a magnetic control power source. The GTAW welding gun, the GMAW welding gun, the magnetic conductive iron core, the magnet exciting coil and the magnetic shoe are fixed through the assembling clamp to form a welding assembly, and the welding assembly is connected with an end effector of the robot. The GTAW welding gun is connected with a GTAW welding power source through a cable. The GMAW welding gun is connected with a GMAW welding power supply through a cable; the excitation coil is connected with a magnetic control power supply through a cable; the GTAW welding power source, the GMAW welding power source and the magnetic control power source are in communication connection with the robot control system. The defect of incomplete fusion in narrow-gap welding of thick stainless steel plates and high-strength steel is effectively overcome, the optimized energy distribution not only refines the weld structure and improves the mechanical property of a joint, but also reduces deformation caused by heat input, and a stable and reliable high-quality welding result can be obtained.

The invention discloses an outdoor robot control system based on a fusion positioning method, which comprises a positioning navigation module, an interaction module and an execution module, and is characterized in that the positioning navigation module is in data connection with the interaction module and the execution module, and the interaction module is in data connection with the execution module; according to the invention, the RTK, UWB and IMU units are fused, and all the units work cooperatively, so that the problem of continuous and accurate positioning of the traffic isolation robot in a complex urban road environment is solved; according to the method, the road scene where the robot is located is judged through the positioning signal attenuation model, precise switching of the corresponding optimal positioning method is achieved, and high-precision positioning of the traffic isolation robot is achieved; according to the invention, the positioning signal attenuation map and the obstacle map are fused into the two-factor environment map, an optimal path which can avoid obstacles and consider the positioning signal attenuation factor is generated, and navigation is carried out, so that the positioning precision and the navigation accuracy are ensured.

The invention discloses an all-terrain photovoltaic power station intelligent weeding robot control system based on shielding real-time monitoring, and relates to the technical field of intelligent planning. Environmental data of a photovoltaic power station are collected, an environmental data set S is constructed and preprocessed, a photovoltaic power station three-dimensional model is constructed, and the three-dimensional model of the photovoltaic power station is constructed according to the three-dimensional model of the photovoltaic power station; using a path planning algorithm to obtain an operation path, collecting operation data of the intelligent weeding robot in real time in the weeding operation process, constructing an operation data set D, and obtaining a path comprehensive score LJ of each grid region according to the operation data set D, and comparing and analyzing the path comprehensive score LJ with a preset first path score threshold LY and a preset second path score threshold LP, evaluating the operation path, and carrying out operation path adjustment on the grid region of which the evaluation result is that the operation path has deviation, so that the instantaneity and the accuracy of the weeding operation are improved, and the weeding operation efficiency is improved.

This patent discloses a computer system for humanoid robot control system design and implementation, featuring a digital humanoid robot with dynamical models and a set of single-input-single-output (SISO) and multi-input-multi-output (MIMO) controllers. The system comprises a main software program, a generative Al humanoid robot intelligence engine, a robot motion path planner module, and a control system simulation engine. It enables efficient design, testing, validation, and implementation of robot control systems, significantly reducing time to market. The system supports seamless upgrades to accommodate new designs and components, enhancing applications in industrial automation, healthcare, public safety, and more, aligning with the goals of the 4th Industrial Revolution.

The invention discloses an AI chat robot control system and method for human resources, and the method comprises the following steps: S1, obtaining and preprocessing recruitment data, and forming a recruitment data set; s2, performing semantic analysis by using a hierarchical Transform model, and generating position-job seeker feature representation; s3, searching and optimizing the Transform model through a neural architecture to obtain an optimized model; s4, calculating position matching scores based on the optimization model, and generating a candidate sorting list in combination with the knowledge graph; s5, calling the MX chat robot to perform intelligent interview on the candidates, dynamically adjusting the questioning difficulty, and obtaining interview data; s6, fusing the job seeker interview score and the position matching score to generate a final candidate recommendation list; and S7, generating a recruitment decision support report based on the recommendation list. According to the invention, the intelligent recruitment method of hierarchical Transform, neural architecture search optimization and MX chat robot cooperative work is adopted, and accurate optimization of position matching and automatic decision support of job seeker interview evaluation are realized.

The invention provides a robot control system and method based on double cores, the robot control system comprises a real-time core, a non-real-time core and a memory, the memory is connected with the real-time core and the non-real-time core, and a shared storage space of the real-time core and the non-real-time core is arranged in the memory; the non-real-time core is used for executing a track information generation class task and writing a generated expected information group into a shared storage space; and the real-time core is used for calling the corresponding expected information groups from the shared storage space in sequence according to the writing sequence of the expected information groups, and executing a motion control type real-time task according to the expected information groups so as to drive each shaft motor of the robot. The real-time core and the non-real-time core communicate through the shared storage space, transmission of the expected trajectory information block is completed, the real-time requirement for obtaining the expected trajectory information block is guaranteed, interference of calculation fluctuation in the non-real-time core to a control thread in the real-time core is avoided, and the certainty and stability of robot motion control are ensured.

In at least one embodiment, under the control of a robotic control system, a gripper on a robot is positioned to grasp a 3-dimensional object. In at least one embodiment, the relative position of the object and the gripper is determined, at least in part, by using a camera mounted on the gripper.

The invention discloses an automatic connector butting and tightening device and a using method thereof. The automatic connector butting and tightening device comprises an equipment base. The workbench is arranged on the equipment base; the robot system is arranged on the base, and the robot system is provided with a mechanical arm and a visual camera; the servo translation system is arranged on the workbench, and the servo translation system is provided with a tightening gun and a tightening head; the robot control system is arranged on the base; wherein the visual camera is used for identifying a target interface and a connector, guiding posture transformation and movement of the mechanical arm and assisting the mechanical arm in completing unmanned automatic taking and alignment of the connector. And the servo moving translation system is used for driving the tightening gun and the tightening head to tighten the connector and the female seat, and unmanned automatic tightening is completed. The connection between the work heads and the female seats of excessive wire harness connectors can be automatically completed, and the connector taking by a mechanical arm can be automatically completed; the nut of the connector is automatically tightened; and the whole process is less in human intervention.

The invention relates to the technical field of machine control, and discloses a chemical safety inspection robot control system and method based on sensor fusion, and the system comprises a data fusion module, a strategy construction module, a strategy analysis module, a real-time monitoring module, an optimization instruction module, and an event determination module. Original data of the chemical environment is fused into standardized data, and structural features are analyzed to identify risk factors; constructing a security situation map, mapping the security situation map to the robot, and determining a control strategy; when the robot begins to inspect, analyzing the control strategy to control the movement adjustment path action and the path safety inspection action of the robot; coding the movement adjustment path action and the path safety inspection action into a safety control instruction; optimizing the safety control instruction to obtain an optimized control instruction; when the robot completes inspection, an environment safety abstract and an abnormal event record are obtained; the control accuracy of the chemical safety inspection robot can be improved.

The invention relates to the technical field of bionic expression robots, in particular to a facial muscle neural network intelligent bionic expression robot control system which comprises a supporting body, a power module, a control module, a muscle neural network and an expression generator. According to the facial muscle neural network intelligent bionic expression robot control system, grid conductors are connected with second sensors, a plurality of intersection points are formed among the grid conductors, each intersection point is distributed and connected with a driving unit, and the tail ends of the grid conductors are electrically connected with first output ports. The driving units generate driving force under the action of the control unit, the first sensor is used for sensing longitudinal force generated by the driving units, and the second sensor is used for sensing transverse force generated by the driving units, so that when the first output port outputs a power difference value through the grid conductor to act on the driving units, the power difference value acts on the driving units. And the power difference value of the adjacent driving units continuously changes, so that the deformation area generates continuous deformation quantity.

A real-world robot control system using artificial intelligence trained on a digital twin, according to the present invention, comprises: an artificial intelligence module that has undergone reinforcement learning by using a virtual robot on the digital twin replicating a real-world robot; one or more sensing units that are provided in the real-world robot and sense physical characteristics and electrical characteristics; a robot data collection unit that collects data from the sensing units; an error calculation unit that calculates an error between the virtual robot and the real-world robot; a disturbance observation unit that monitors an input signal transmitted from the module to the real-world robot, an output signal output from the real-world robot, and the error and estimates disturbance to generate feedback data for compensating for the disturbance when the disturbance is greater than or equal to a threshold value; and a robot driving module that transmits a control command for controlling the real-world robot by reflecting a control signal from the artificial intelligence module and the feedback data generated from the disturbance observation unit.

The invention discloses a robot control system and a robot, the system comprises an upper computer and a lower computer, the lower computer is used for obtaining data from various sensors, decoding and operating the data, uploading message data to be uploaded to the upper computer, obtaining instructions from the upper computer, and executing control operation according to the instructions. A native system application, a message transfer service and a robot operating system (ROS) application are arranged in the upper computer, the native system application can restore and integrate existing native system applications of a team, and various mature algorithms and tool resources of the ROS can be fully utilized through the ROS application. The lower computer and the native system application in the upper computer carry out message communication through a first communication protocol, the message transfer service is responsible for communication adaptation between the native system application and the ROS application, and communication interaction between the native system application and the ROS application is achieved through conversion of message formats.

Various surgical systems are disclosed. A surgical system comprises a robotic tool, a robot control system, a surgical instrument, and a surgical hub. The robot control system comprises a control console and a control unit in signal communication with the control console and the robotic tool. The surgical hub comprises a display. The surgical hub is in signal communication with the robot control system. The surgical hub is configured to detect the surgical instrument and represent the surgical instrument on the display.