811 results about "Robot control system" patented technology

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 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.

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 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 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 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.

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.

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.

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 bridge support disease detection system and method based on a rail-mounted inspection robot, and belongs to the technical field of bridge detection. The system comprises a robot walking guide mechanism, the rail-mounted inspection robot and a self-adaptive following system; the rail type inspection robot comprises a chassis, and a mechanical arm is arranged on the chassis; the self-adaptive following system comprises a robot control system, an image processing system and a network communication system; the robot control system comprises a main control unit which is mainly used for controlling movement of a chassis and self-adaptive adjustment of a mechanical arm; the image processing system comprises an image acquisition device, an image processing and storage device and an edge computing device; and the image acquisition equipment is arranged on the mechanical arm and is mainly used for acquiring image data. According to the invention, intelligent identification and adaptive following can be carried out on the bridge support disease image, so that the image acquisition equipment can be ensured to lock and track the disease area from the front side, and high-quality and omission-free photographing detection can be realized.

The invention relates to a biomimetic robot control system based on digital human action mapping, in particular to the technical field of robot control, solves the problem that a biomimetic robot is difficult to perceive physical attributes only by means of vision, achieves accurate pre-judgment of contact force and material attributes by analyzing digital human fingertip microscopic color change and object texture deformation, and improves the accuracy of the biomimetic robot control system. A robot can establish an adaptive mechanical model before contact, the system dynamically adjusts the flexibility and damping of a mechanical arm according to the environment rigidity and friction characteristics, soft grabbing of hard objects and stable grabbing of sliding objects are achieved, the hysteresis quality of traditional feedback control is effectively overcome, meanwhile, the time sequence feed-forward and energy monitoring mechanism is combined, and the control precision is improved. Collision protection is provided while zero-delay synchronization of actions is ensured, and the operation stability and safety of the robot for fragile and flexible objects in a complex environment are remarkably improved.

The invention relates to the technical field of cargo unloading control technologies, and discloses a control system and method for a cargo unloading robot of a van truck. A van truck cargo unloading robot control system comprises an acquisition module, a main control module, an execution module and a light supplementing module. A control method of a van truck cargo unloading robot comprises the steps that multi-mode three-dimensional point clouds of a truck carriage and cargoes are obtained, the multi-mode three-dimensional point clouds are preprocessed, and ordered three-dimensional point clouds are obtained; performing path planning on the ordered three-dimensional point cloud through improved RRT * to obtain an initial path trajectory; optimizing the initial path trajectory to generate a predicted path trajectory; and based on the predicted path trajectory, data analysis is carried out through adaptive feedback PID control, and optimal control parameters of the mechanical arm are obtained. The unloading efficiency is effectively improved.

To provide a robot control system, etc. capable to contributing to reducing costs and causing to synchronize movements between robots. The system comprises: a plurality of mobile robots; a control apparatus that is communicably connected to the plurality of mobile robots and manages and controls the plurality of mobile robots; and a sensor apparatus that is communicably connected to the control apparatus and senses the plurality of mobile robots in a predetermined area. The control apparatus executes processings of: generating control information including information related to a current location and an intermediate destination of each of the plurality of mobile robots at the same timing based on information from the sensor apparatus; and transmitting the generated control information to each of the plurality of mobile robots.

The invention belongs to the technical field of robots, and particularly relates to a global trajectory strategy reinforcement learning method and a robot control system, and the method comprises the following specific steps: S1, environment modeling and state definition: firstly collecting environment data, then defining a state space of a robot according to the collected environment information, the position, the posture, the sensor data and the task target information of the robot are coded and converted into a state vector which can be processed by an algorithm; s2, setting an action space: determining all actions which can be executed by the robot in a scene, and discretizing or continuously processing the actions to form the action space; according to the method, the path planning response speed can be increased, collision can be effectively avoided, and the adaptive capacity of the robot in a dynamic environment can be greatly enhanced.

The invention provides an aerial operation robot control system and method for water quality monitoring. The control system provided by the invention comprises a collaborative and autonomous navigation module which accurately plans a water quality monitoring flight path according to the monitored water area and position point information, and intelligently starts a fine monitoring module as required according to the regional water quality analysis result of a remote sensing monitoring module, thereby guaranteeing the comprehensive coverage of a monitored region, and timely focusing an abnormal region. The monitoring efficiency is greatly improved; the multi-rotor unmanned aerial vehicle control module guarantees stable flight; and the mechanical arm control module assists in accurate measurement. The remote sensing monitoring module and the fine monitoring module respectively utilize different sensors to obtain regional and point water quality data. The data fusion analysis module integrates the two types of data, determines and corrects the pollution range and source, locks the pollution source according to the pollution gradient, and improves the efficiency and precision of water quality monitoring.

The present invention discloses an adaptive memory-enhanced imitation learning method for robot control tasks, including standardizing original multimodal data and extracting temporal features to construct hierarchical feature representation; establishing a hierarchical structure including short-term memory, working memory and long-term memory, evaluating memory importance based on usage frequency, task relevance and novelty, and dynamically updating memory content; retrieving relevant memory according to the current state and performing multimodal feature fusion; calculating shaping rewards based on neighbor sample search; and optimizing strategy network parameters through multi-loss function combination. The present invention improves the learning efficiency and execution performance of the robot control system through adaptive memory management and multimodal feature fusion mechanism.

The invention relates to the technical field of automatic welding, and discloses a micro welding seam tracking method and system based on electromagnetic induction. According to the method, a high-frequency alternating magnetic field is applied to a welding seam area through an exciting coil in the micro electromagnetic sensing core; an induction coil in the electromagnetic sensing core is used for detecting magnetic field disturbance caused by geometric shape changes of the weld groove, and induction signals are obtained; processing the induction signal through a signal processing circuit integrated in the probe, namely converting the impedance change of the coil into a voltage signal, demodulating and filtering the voltage signal, and extracting a direct-current voltage signal corresponding to the deviation of the probe relative to the central position of the welding seam; the direct-current voltage signals serve as position feedback information to be output to a welding robot control system; and the movement track of the welding robot is adjusted in real time according to the position feedback information, and welding seam tracking and path deviation correction are achieved.

The invention provides a robot control system, a data processing method, electronic equipment and a program product, and relates to the field of robot control. According to the robot control system, a first system obtains environment sensing data and task description data of a target robot, after feature fusion, a state vector is generated at a first operation frequency and sent to a second system and a third system; and the second system receives the state vector and the environment perception data and generates description data of the target object at a second operation frequency higher than the first operation frequency. And the third system receives the state vector, the environment sensing data and the state data of the target robot, generates a control instruction of the target robot at a third operation frequency higher than the first operation frequency, and drives the target robot to execute corresponding actions. According to the method, the real-time perception of the target robot on the target object in a complex task is remarkably enhanced, and a more efficient and accurate solution is provided for a task with a body operation.

The invention relates to a robot control system and method, a storage medium and electronic equipment. The system runs in a heterogeneous system, the heterogeneous system comprises a non-real-time system and one or more hard real-time systems, a first control system runs on the non-real-time system, and a real-time control framework runs on the hard real-time system; the real-time control framework comprises a control communication module, a motion algorithm module and a distributed communication module; the hard real-time system sends collected feedback data of the robot to a first control system on a non-real-time system by calling a sending function in a real-time transmission protocol of a distributed communication module; a control instruction of the first control system is received by calling a receiving function in a real-time transmission protocol of the distributed communication module; and the control instruction is sent to the robot through the control communication module. The technical problem that a soft real-time system is poor in real-time performance and unstable when controlling a robot is solved.

A supra-aortic vessel access robotic control system includes a guidewire hub configured to adjust each of an axial position and a rotational position of a guidewire; a guide catheter hub configured to adjust a guide catheter in an axial direction; and a procedure catheter hub configured to adjust each of an axial position and a rotational position of a procedure catheter, and also to laterally deflect a distal deflection zone of the procedure catheter.

The invention discloses a sweeping robot cleaning control system with a self-adaptive mode adjustment function, and relates to the technical field of intelligent cleaning robot control systems, and the system comprises a multi-mode sensing module, a central processing module and a cleaning execution module. The multi-modal sensing module collects multi-dimensional data such as a hyperspectral image, acoustic response and gas concentration of ground stains, and the central processing module extracts features through the data fusion unit and deeply fuses the features to generate analysis results of stain types, adhesion strength and chemical components. The decision control unit dynamically generates a cleaning instruction in combination with a preset strategy and the real-time state of the robot, the cleaning execution module executes a targeted cleaning action, and meanwhile, the system constructs a dynamic stain atlas to achieve historical data reuse and prospective adjustment, so that the cleaning efficiency and the resource utilization efficiency are remarkably improved, and the cleaning effect is improved. The method is suitable for intelligent cleaning scenes of home and office environments.