626 results about "Robot control system" patented technology

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 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.

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 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.

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 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 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.

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 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 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.

The invention discloses a multi-robot control system, and relates to the field of robot path planning. According to the system, in a robot path driving task, candidate path nodes are selected by randomly switching target guidance and a global exploration mode, feasible nodes and path segments are determined by combining historical path nodes, and the path nodes are expanded after collision detection of a static obstacle and a dynamic robot. The system also dynamically adjusts a driving step length based on a load rate, sets a safety margin including load compensation, and realizes multi-machine cooperation through local path topological optimization and distributed decision. According to the method, the multi-robot path conflict and collision risk is effectively reduced, the heavy-load working condition motion stability and the system cooperation efficiency are improved, and the method is suitable for a multi-robot cooperation operation scene.

The invention relates to the technical field of robot control systems, in particular to a robot whole-body motion control method based on reinforcement learning, which comprises the following steps: collecting inertial measurement unit data and motor feedback data in real time through a reasoning node; inputting the inertial measurement unit data and the motor feedback data into a deep reinforcement learning model accelerated by TensorRT, and generating a control instruction of the motor; the control instruction is issued through a master station node by using an Ethernet control automation technology bus, and the upper layer of the master station node runs a four-state state machine including an initialization state, an enabling state, a motor control state and a fault state; according to the invention, high-frequency real-time control, multi-scene self-adaptive adjustment and multi-level safety protection of whole-body motion of the robot are realized, and the problems of poor generalization, low real-time response speed and single safety protection mechanism in traditional robot control are solved.

The invention provides an intelligent all-in-one machine with a body, a system and an operation method, and belongs to the technical field of industrial automation and robot control systems.The all-in-one machine comprises an automation system IDE module, an automation system driving module, a robot body driving module and a function supply module, the function supply module is used for providing a data acquisition function, a simulation task execution function, a model training function and a model reasoning function with time synchronization for the robot body driving module according to a control command which can be executed by the robot; the intelligent all-in-one machine solves the problem of time synchronization between the all-in-one machine and the robot, between the acquisition module and the robot and between the acquisition module and the multi-frequency sensor data, and ensures that the time synchronization between the acquisition module and the robot and between the acquisition module and the multi-frequency sensor data is realized in the sensor data acquisition, model reasoning and task execution processes through accurate timestamp management and a multi-level time synchronization mechanism. Millisecond-level time alignment of data of all the modules can be achieved, and control errors caused by time drift and data delay are effectively avoided.

The present invention relates to the field of robot control and proposes a humanoid robot control system and method based on reinforcement learning. The humanoid robot control system includes a first control subsystem and a second control subsystem; the first control subsystem includes a strategy reasoning module, a state transition module and a robot control module; the second control subsystem includes a data acquisition module and a drive control module; the data acquisition module is used to complete the timestamp alignment and outlier filtering of sensor data, and transmit them to the state transition module; the state transition module is used to fuse multi-source sensor data and construct a timing state feature containing real-time measurement values ​​and historical timing information; the strategy reasoning module is used to generate a multi-joint angle target value of the robot according to the timing state feature provided by the state transition module; the robot control module is used to parse the multi-joint angle target value output by the strategy reasoning module, select a control mode, and generate a control command including a parameter adjustment instruction.

The invention discloses a robot control system parameter optimization training system based on machine learning, and relates to the technical field of intelligent control. The system comprises a data acquisition and structured marking module, a coupling influence strength evaluation module, a deviation mapping model construction module, a neural network training module, a real-time prediction and trajectory correction module and a closed-loop control execution module, and is characterized in that joint data is acquired in real time, a lightweight machine learning model is constructed and trained to predict trajectory deviation caused by dynamic coupling, and the trajectory deviation is corrected. According to the method, a target trajectory is dynamically corrected, closed-loop online compensation is achieved through a bottom layer controller, the whole process from data collection and prediction to trajectory correction and bottom layer control execution is continuously and circularly executed, and therefore closed-loop online compensation of the joint dynamic coupling effect is achieved. The objective of the invention is to solve the problems of trajectory tracking and dynamic response precision caused by dynamic coupling of multi-joint robots.

The invention discloses a hand-eye cooperative robot control system and method based on dynamic operator arrangement, an upper computer planning layer operates a master control computer to periodically trigger task scheduling, a joint controller of a lower computer execution layer receives an instruction through a redundant bus to perform servo control, hand-eye camera data triggers a visual assembly line through an interrupt event, and a visual assembly line is controlled through a control interface. According to the method, pressure is calculated through visual processing, motion planning and joint control in a load sharing mode, a double-buffering mechanism is adopted to enable current frame visual processing and previous frame motion control to be executed in an overlapping mode, real-time scheduling and parallel computing of tasks are completed, hot data are cached in a memory database, cold data are archived to an HDFS and migrated through an LRU strategy, and the real-time scheduling and parallel computing of the tasks are completed. Data type conversion is automatically derived and performed based on a feature type registry, and conditional branch execution is triggered according to real-time sensor data. According to the control system, cross-hardware plug and play, algorithm flow configurability and data flow real-time sharing are achieved, and the requirement for improving the efficiency of complex operation tasks is met.

The invention discloses a full-process closed-loop nursing assistant robot control system based on multi-modal interaction. The system comprises a multi-modal sensing module, a facial expression analysis module, an action evaluation module, a closed-loop control module, an execution driving module and a data interaction module. The multi-modal sensing module integrates multi-source information to realize accurate feature fusion; the facial expression analysis module generates expression feature vectors; the action evaluation module calculates action normative parameters; the closed-loop control module generates a dynamic task queue and distributes instructions; the execution driving module responds to the instruction to complete operation; and the data interaction module realizes data transmission and storage. The system overcomes the defects that in the prior art, multi-modal information fusion is insufficient, and control closed-loop real-time performance and adaptability are poor, nursing accuracy and efficiency are improved, accurate and personalized nursing requirements are met, manual nursing defects are made up, and operation normalization is unified.

The invention provides a robot action generation method and a robot control system, and the method comprises the steps: obtaining multi-modal observation data at a current moment; the modal weight and the initial noise trajectory of each modal are acquired; and according to the multi-modal observation data, the modal weight of each modal and the noise estimation model corresponding to each modal, performing iterative exploration on the initial noise trajectory according to a time inverse sequence, and generating an action trajectory sequence of the robot. According to the method, noise estimation of the pre-trained single-mode diffusion strategy can be combined during reasoning, and information of different modes can be efficiently fused, so that on the premise of avoiding complexity of multi-mode data collection and joint training, multi-mode strategy fusion is realized, and meanwhile, the multi-mode data collection and joint training efficiency is improved. And the flexibility, the real-time adaptability, the robustness and the expandability of the action generation process are enhanced. Moreover, by avoiding multi-modal joint training, the training cost and the computing resource demand are greatly reduced.

The invention discloses an AI-driven industrial robot intelligent assembly collaborative scheduling method and system, and the method comprises the steps: collecting assembly operation data, and constructing a semantic structure which comprises a process, a robot and a space region; extracting time sequence data, inputting the time sequence data into the prediction model, calculating offset and mapping a shell state; constructing an origami surface structure, analyzing space and time conflicts, and determining conflict positions; rearranging execution space and time windows, generating constraint mapping, and constructing a regular space-time layout; constructing a two-dimensional scheduling texture block, executing a reconstruction operation, and generating a scheduling texture sequence; and generating a scheduling scheme, completing constraint verification, and issuing the scheduling scheme to a robot control system for execution. According to the method, by introducing a beat prediction and state driving cooperative scheduling mechanism based on assembly semantic data, advanced perception and stable cooperative control of beat fluctuation, space conflicts and scheduling disturbance in the assembly process of multiple industrial robots are achieved.

The invention belongs to the technical field of medical robots and minimally invasive surgery, and provides a robot control system and method for blue laser vaporization surgery of prostatic hyperplasia. Mapping the nuclear magnetic image volume data to a deformation field under an ultrasonic acquisition coordinate system, and deforming the preoperative nuclear magnetic image to an intra-operative ultrasonic space by using the deformation field to complete image registration; fusing the registered image with a stereoscopic vision system; the spatial depth of the surface of the target tissue is obtained from the endoscopic image so as to supplement navigation information; according to the utility model, the prostate deformation and probe posture change adaptive capacity in an operation is improved, the real-time visual closed-loop regulation and control capacity is improved, and the characteristics of small light spots, shallow heat diffusion, excellent hemostasis and the like of blue laser are combined, so that the vaporization and hemostasis precision in a tiny blood vessel dense area is ensured, and the problems of large tissue trauma and the like are avoided.

The invention discloses a three-mode switching type sewage plant biochemical pool inspection robot control system, and belongs to the technical field of robot inspection, the three-mode switching type sewage plant biochemical pool inspection robot control system comprises a composite moving system, an integrated crawler walking module, a buoy module and a propeller module, and the three-mode switching type sewage plant biochemical pool inspection robot control system is used for controlling and switching walking modes of an inspection robot; the mode switching triggering mechanism is used for switching the composite moving system to the crawler walking module, the buoy module or the propeller module according to the water depth; the multi-source heterogeneous sensor monitoring module is used for detecting environmental data in real time through a multi-source heterogeneous sensor; and the path planning module is used for fusing monitoring data of the multi-source heterogeneous sensor, optimizing a global path by adopting an improved ant colony algorithm, and carrying out dynamic obstacle avoidance in combination with a D * Lite algorithm. Switching of multiple propelling modes is supported, and limitation of a single moving mode of a traditional robot is broken through; and meanwhile, monitoring data of heterogeneous sensors are aligned, and dynamic obstacle avoidance is performed by adopting an improved ant colony algorithm in combination with a D * Lite algorithm, so that the accuracy of path planning is improved.

The invention discloses an amphibious robot power system fault prediction method based on land and water working condition difference, and belongs to the technical field of robot control systems, and the method comprises the steps: sensing land and water physical field characteristics in real time, and emerging an environment coupling topology state; monitoring an energy and information exchange flow, and obtaining a flow mode divergence as a fault precursor; receiving land and water environment switching identification, inducing relaxation and tending to a new convergence steady state; taking the new steady state as an initial condition, taking the flow mode divergence and the energy dissipation rate as evolution driving potential, and generating a minimum action quantity track; and driving parameter or weight adjustment along a minimum action quantity track to realize structural adaptation and toughness internalization. According to the method, a control strategy integrating dynamic topology modeling, prospective risk assessment and structural learning adaptation is adopted, the active fault prediction capacity and the self-adaptive stability in the land and water environment switching process can be improved, and the overall reliability and toughness of the robot are enhanced.

The invention relates to the technical field of robot control, in particular to a robot self-adaptive motion control algorithm based on reinforcement learning, and the algorithm obtains a torque signal of a robot joint, constructs a state vector representing an interaction state of a robot and an environment based on the torque signal, and achieves the self-adaptive motion control of the robot through a layered reinforcement learning training mechanism. The method comprises the following steps: generating a self-adaptive motion control strategy which comprises the steps of constructing a multi-dimensional reward function and fusing task completion degree, energy efficiency, motion safety and smoothness, carrying out iterative optimization on a mapping relation between a state vector and a motion control sequence by utilizing a Q-learning algorithm, verifying the self-adaptive motion control strategy in various physical parameter environments, and carrying out self-adaptive motion control according to the self-adaptive motion control strategy. And the self-adaptive motion control strategy passing the verification is deployed to the robot control system, so that the self-adaptability is remarkably improved, the robot control strategy can adapt to physical parameter changes within the range of + / -30%, and the parameters can be readjusted without manual intervention.

The invention discloses a double-arm robot main and auxiliary moving operation coordination control method based on vision / force sense servo and implementation thereof. Double-arm coordination is supported, multi-sensing information is fused, compliant operation is considered, and the method is adaptive to a moving scene. The mobile main and auxiliary double-arm robot control system comprises a mobile base subsystem, a main and auxiliary double-arm subsystem and a control subsystem. The double-arm main and auxiliary operation coordination control method based on visual / force sense servo comprises visual servo target tracking based on a tail end camera and force sense servo compliant operation control based on a force sensor, operation tasks are achieved in the state that a base moves, and the double-arm main and auxiliary operation coordination control method adapts to the complex working environment; a state machine mechanism is adopted to manage a mechanical arm motion mode, screw assembly task-oriented operation process arrangement is realized, and the efficiency and controllability of the operation process are improved.