364 results about "Robotic control" patented technology

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

The embodiment of the invention provides an optimization method and system for a manipulator control system, and the method comprises the steps: firstly obtaining an operation data set containing a control instruction sequence and corresponding motion state data in a historical operation period of a manipulator, carrying out the feature extraction of the operation data set, and obtaining an associated motion feature set; calling a pre-trained control strategy optimization model to analyze and generate strategy adjustment parameters, adaptively updating the control instruction sequence of the current operation task according to the strategy adjustment parameters to obtain a target control instruction sequence, and finally inputting the target control instruction sequence into a control system to execute operation. And real-time motion state data is collected for subsequent iterative optimization, so that the performance of the manipulator control system can be continuously improved, and the operation precision and efficiency are improved.

The invention provides an industrial robot multi-station intelligent sorting and stacking method based on visual guidance, which relates to the technical field of robot control, and comprises the following steps: determining a target station by acquiring multi-station real-time state information, calculating an optimal grabbing time window by using a graph neural network state predictor, and obtaining a target grabbing time window; and the placement strategy is dynamically adjusted based on the visual image and the force sensing data in the article stacking process. According to the multi-station intelligent stacking system, the sorting efficiency in a multi-station scene can be improved, the grabbing precision of dynamic objects is enhanced, and stable and reliable intelligent stacking operation is achieved.

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.

Disclosed are systems and methods to control a robotic device using natural language commands. A natural language command may be received by a system. The system may convert the command into low-level machine controls and logic for implementation by a robotic device to achieve a desired action. In some instances, an API module may include mapping data to associate high-level commands with low-level machine controls. A language model may process the natural language command (input), high-level commands, and / or other information, such as system state, sensor observation data, parameters, etc., to determine one or more commands to execute by a robotic device and possibly logic for execution by the robotic device. The robotic device may receive the low-level machine controls and logic to cause the robotic device to perform the requested actions.

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 field of robot technology and artificial intelligence, and particularly discloses a thumb independent optimization five-finger manipulator control method based on hierarchical strategy control. The method comprises the following steps: S1, designing a hierarchical strategy control algorithm framework, and obtaining current target object information; s2, dividing a high-level strategy network and a low-level strategy network, wherein the high-level strategy network determines whether to activate the action of a thumb; the low-layer strategy network outputs the action probability of each finger joint based on the decision of the high-layer strategy network; s3, evaluating a strategy optimization effect by using an evaluation module; S4, carrying out training and optimization; s5, repeating the training process in S4; s6, updating the strategy according to the collected experience after the action is executed every time; s7, after training is completed, the model of each finger and strategy parameters learned in the training process are stored; and S8, verifying the performance of the trained model in a simulation environment. According to the invention, the problem of uncoordinated actions caused by excessive coupling between fingers can be effectively solved.

The invention provides an automatic grabbing control method for a hot-melt gasket of an automatic laying trolley, and relates to the field of automatic control, which comprises the following steps: S1, acquiring the position of the hot-melt gasket through image acquisition by taking the set position of a manipulator as a center, when the two-dimensional coordinate of the hot melting gasket position is the same as the two-dimensional coordinate of the set position of the manipulator, the manipulator control system starts the control method; s2, the vertical height value between the real-time position of the manipulator and the position of the hot melting gasket is calculated; calculating the current error height between the current descending height of the manipulator and the target descending height according to the vertical height value; s3, the current error height is fed back and input into a manipulator control system; and S4, the manipulator control system outputs a control signal based on the current error height, the position of the manipulator is accurately controlled through the control signal, the step S2 is executed again until the target descending height is reached, and automatic grabbing control over the hot melting gasket is achieved. The performance of the system is remarkably improved, and the efficiency and robustness of the manipulator control system are improved.

The invention discloses a continuous feeding manipulator control method which comprises the following steps: constructing a finite element simulation model aiming at each joint of a mechanical arm and a tail end execution structure, acquiring inherent frequency distribution data of the structure under different load states and posture working conditions, and generating a frequency sensitive area parameter set based on the data. The method has the advantages that the track segment exciting the resonance risk can be recognized before the path is executed, the inherent frequency of the structure is effectively avoided through frequency matching and path reconstruction, the dynamic adaptability is improved, and pose offset and structural fatigue caused by resonance are prevented. Meanwhile, the system has the self-adaptive closed-loop optimization capacity, disturbance injection testing and periodic response verification are combined, the structure state can be monitored in real time, path re-optimization can be triggered, the stability and precision of the mechanical arm in continuous high-frequency operation are ensured, and the overall reliability and intelligent level of a production line are improved.

Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

The invention discloses a fault-tolerant reconstruction method for sensing faults of a spatial dexterous hand, and relates to the field of robot control. In order to solve the defects that in the prior art, a dexterous hand on-orbit sensor is weak in fault-tolerant capability, insufficient in model generalization capability and incapable of being adaptive to complex space environment changes, the technical scheme provided by the invention comprises the following steps: constructing a conditional variation auto-encoder model, inputting a joint position and a rope length sequence, and constructing a model; time sequence features are extracted, and a reconstructed joint position mean value and a prediction variance are output; the reconstructed joint position mean value is compared with actual joint data, a dynamic confidence interval is generated, and whether the data breaks down or not is judged; for the data judged to be normal, information entropy is calculated based on the prediction variance, and data samples with the information entropy exceeding a set threshold value are screened and written into a dynamic buffer area; and when the buffer data is fully loaded, incremental learning is triggered, and model parameters are updated by utilizing the buffer data. The method is suitable for real-time detection and fault-tolerant reconstruction of sensor faults in the long-term on-orbit operation process of the space dexterous hand.

The invention discloses a sorting manipulator intelligent control system based on big data, and the system comprises a multi-source data collection module which is used for synchronously collecting the real-time operation parameters of a sorting manipulator, the multi-modal feature data of a to-be-sorted object, and the dynamic environment parameters; the cross-modal fusion module is used for performing space-time alignment on the multi-modal feature data through a dynamic weighting algorithm; the double-domain cooperative training module is used for constructing a mapping relation between a virtual simulation domain and a physical entity domain, migrating initial model parameters trained by the virtual domain to the entity domain based on transfer learning, minimizing double-domain data distribution difference through a domain adaptation loss function, and generating an intelligent decision model adaptive to an entity scene; and the adaptive control module is used for generating a real-time control sequence through a model prediction control algorithm according to the sorting path planning result output by the intelligent decision model. According to the invention, more intelligent sorting manipulator control is realized.

One or more devices, systems, methods, and storage mediums for performing robotic control and / or using force and / or force difference determination(s) to estimate / determine exerted force by using wire force information are provided herein. Examples include, but are not limited to, use with a continuum robot or catheter having at least one independently manipulatable, bendable section for advancement through a passage, without contacting fragile element(s) within the passage, where one or more driving wires of the catheter have force(s) and / or force difference(s) to be determined / estimated. Examples of applications include imaging, evaluating, and diagnosing biological objects, such as, but not limited to, for bronchial or other medical applications. Techniques provided herein also improve processing, imaging, and catheter control efficiency while achieving images that are more precise, and also achieve devices, systems, methods, and storage mediums that reduce mental and physical burden and improve ease of use.

The invention provides a robot control method, system, device and equipment and a medium, and the method comprises the steps: carrying out the visual analysis of a task scene where a robot is located based on a user instruction through a trained target visual language model, so as to obtain a task list needed for achieving the user instruction, the target visual language model is constructed based on a visual encoder, a visual language alignment layer and a large language model; based on the tasks in the task list, a control instruction needed by the robot for executing the task list is obtained; and through diffusion of the control instruction by the diffusion model, determining a mechanical motion track corresponding to the control instruction so as to perform operation control on the robot. The understanding ability of the robot for natural languages is improved, the adaptive capacity of the robot for task scenes is improved, the control precision of the robot is improved, the control flexibility of the robot is improved, and the achievement degree of the robot for user instructions is improved.

A variety of smart inference systems and robotic control devices are disclosed. A device networking system interconnects mobile devices, robotic devices, and others. A robotic controller secures and manipulates a controlled device user interface. A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality7 of capabilities. A smart payment processor system assigns payments accounts automatically as a function of a semantic matching between one or more inferred semantic identities. A robotic emulation device captures and analyzes a video signal from a target device and transmits manipulation signals emulating a peripheral input device. A system of carts are physically coupled in a charging configuration. A conveyor is configured to couple with another conveyor and having sensors to detect weight, size, location or other aspects of conveyed items, and implementing semantic analysis for management and augmentation.

The invention relates to the technical field of intelligent conveying, and discloses a manipulator and belt conveying cooperative control system for building block processing, which comprises a manipulator control unit for controlling the rotation and grabbing actions of a manipulator; the belt control unit is used for controlling start and stop of the production process belt and the stacking process belt conveying assembly; the position detection unit is used for detecting the positions of the building blocks on the belt; the visual detection unit is arranged at a detection station of a production process belt and is used for acquiring an image of the building block to be transferred; and the quality evaluation unit is used for receiving the image information of the to-be-transferred building blocks and evaluating the forming quality of the building blocks. Forming defects of the building blocks are accurately recognized, unqualified products can be screened out in the conveying process, waste of subsequent procedures is avoided, and the overall quality of the factory building blocks is more uniform; the position detection unit, the mechanical arm control unit and the belt control unit are adopted for cooperative operation, the building block conveying and transferring process is closely linked and accurate, and the conveying and production efficiency is improved.

The invention provides a system for controlling a manipulator in real time through hand electromyographic signals, which comprises a data acquisition unit, a terminal equipment unit and a tail end execution unit which are in communication connection through serial ports. The data acquisition unit comprises a data acquisition device and a signal acquisition module, and is configured to acquire a multi-channel electromyographic signal of a hand of a user in real time and perform signal conditioning and digitization so as to obtain a digital signal sequence representing a muscle activity intention of the user; the terminal equipment unit comprises a data preprocessing module, a model training module, a gesture recognition module and an instruction generation module; the tail end execution unit comprises a manipulator control unit which is in communication connection with the control output module and is configured to be used for receiving the manipulator control instruction and driving a manipulator to execute corresponding actions; in combination with a control method, the problems of insufficient real-time performance, poor hand action recognition precision and robustness, unstable control instruction, poor user adaptability and the like in the prior art are solved, and the man-machine interaction performance is improved.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The invention provides a method and device for preparing a composite board based on laser remelting and ultrasound, and relates to the technical field of composite board preparation. The method specifically comprises the steps that a molten pool is formed on a first metal plate to be compounded through laser remelting, a fiber laying manipulator control system is started, a fiber laying manipulator is made to lay carbon fibers into the molten pool according to a preset track of a laser remelting head, ultrasonic vibration is started at the same time, and a preformed composite plate is obtained; the molten pool on the preformed composite board is solidified through a temperature control system, ultrasonic vibration is started at the same time, and a metal / carbon fiber mechanical-metallurgical composite interface is formed; and a second metal plate to be compounded is compounded through roller pressure, and the metal / fiber / metal three-layer composite plate is obtained. The device comprises a laser remelting and fiber laying manipulator, a temperature control system and an ultrasonic vibration probe. According to the invention, laser remelting and ultrasonic vibration are combined to form a metal / carbon fiber mechanical-metallurgical composite interface, so that the interface strength of the composite board is enhanced, and the practicability of the composite board is improved.

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

The present application discloses a robotic control system, and a method and a computer system for controlling a robot. The robotic control system includes a memory and one or more processors coupled to the memory. The memory is configured to store configured to store a model of a robot having a plurality of axes of control including at least a linear axis and one or more rotational axes. The one or more processors are configured to use the model to control the robot to perform a task, including by sending to the robot a set of control signals to cause the robot to move with respect to two or more of said axes of control including at least the linear axis.

The invention relates to the technical field of oil and gas drilling and production equipment, in particular to an oil pipe remote buckling system and method for under-pressure operation and under-pressure operation equipment. The buckling system comprises a lifting device, a manipulator, a guide device, a camera and a console. And the camera is used for acquiring a working image of the buckling system. The console controls the lifting device, the guiding device and the mechanical arm according to images collected by the camera. The console controls the lifting device to suspend and drive the oil pipe; and the manipulator is controlled to centralize the moving oil pipe, and the guide device is controlled to guide the suspended oil pipe. In the buckling process, an oil pipe can be centralized, the oil pipe can be prevented from colliding with facilities on a wellhead operation platform, an operator can remotely control the lifting device, the mechanical arm and the guide device through the control console, and in the buckling operation process, the oil pipe can be straightened, and the safety is improved. Operating personnel do not need to be exposed on a wellhead operation platform for under-pressure operation, and the safety risk of the operating personnel is reduced.

Techniques are disclosed that enable the generation of predicted sequences of terminals using a generator model portion of a prediction model. Various implementations include controlling actuators of a robot based on the predicted sequences of terminals. Additional or alternative implementations include jointly training the generator model portion of the prediction model using a discriminator model portion of the prediction model using, for example, stochastic adversarial based sampling.

The invention discloses a manipulator control method and device and electronic equipment, and the method comprises the steps: obtaining a first task of a manipulator, and determining a first neural network according to the first task; reconstructing the first task by using the first neural network to obtain a control strategy of the reconstructed first task, the control strategy comprising a series of basic control instructions; control parameters for controlling the manipulator are obtained on the basis of the control strategy, the manipulator is controlled to execute a control action according to the control parameters, and the control parameters comprise parameters corresponding to the basic control instruction. Due to the fact that the neural network model is utilized, self-learning of mechanical arm control can be achieved, the flexible and changeable control requirements of the mechanical arm are met, corresponding control can be accurately conducted, and due to the fact that the control is completed through the server, the requirement for the computing power of the mechanical arm can be lowered, and the application range is wider.

The invention relates to the technical field of cosmetic container production control, in particular to a multifunctional moving manipulator, a control system, a control method and a bottle cap moving all-in-one machine of the multifunctional moving manipulator. N bottle caps are adsorbed through N vacuum suction nozzles arranged on a second base plate, and then the adsorbed bottle caps are moved to a conveying belt through a multi-axis mechanical arm till the bottle caps on a bottle cap tray are completely moved, and feeding is completed; and when the bottle caps on the bottle cap tray on the uppermost layer are moved, the grabbing structure on the mechanical front arm is controlled again to move to the position above the bottle cap trays, the N vacuum suction nozzles and the M vacuum suction heads are used for adsorbing the vacant bottle cap trays at the same time, and the bottle cap trays which are adsorbed to be vacant are moved away through the multi-axis mechanical arm. According to the scheme, the bottle caps and the trays can be automatically and integrally moved respectively, the feeding efficiency of the cosmetic bottle caps is improved, and continuous supply of the bottle caps is guaranteed.

The invention discloses a manipulator control method and device and electronic equipment, and the method comprises the steps that a first task is acquired, a first neural network is determined according to the first task, a plurality of pre-trained neural networks are configured in a control unit of a manipulator, and the first neural network is a neural network matched with the first task in the neural networks; reconstructing the first task by using a first neural network to obtain a control strategy of the reconstructed first task, the control strategy comprising a series of basic control instructions; control parameters for controlling the manipulator are obtained based on the control strategy, the manipulator is controlled to execute the control action according to the control parameters, and the control parameters comprise parameters corresponding to the basic control instruction. Due to the fact that spatial position control is completed through the neural network configured in the manipulator control unit, the method can well adapt to application scenes without networking, and computing resources of the control unit do not need to be additionally consumed.

The invention discloses a waste battery discharging device based on a mechanical arm, and relates to the technical field of waste battery disassembling and recycling. The device comprises a base, a conveying belt is mounted between the two sides of an inner cavity of the base, and a mounting frame is fixedly connected to the top of the base. Visual support is provided for the manipulators through the discharging assembly and the industrial camera, the manipulators can accurately find the positive and negative electrode connectors of the waste batteries conveyed by the conveying belt, the two manipulators control the fixing sleeve to move to the position over the positive and negative electrode connectors, then the fixing sleeve is controlled to move downwards, and contact rods in the fixing sleeve make contact with the positive and negative electrode connectors; the contact rods making contact with the positive and negative electrode connectors compress the springs through the pressing rods and move upwards, the contact rods not making contact with the positive and negative electrode connectors are in a normal state, the positive and negative electrode connectors are wrapped with the multiple contact rods, and the positive and negative electrode connectors of different specifications and sizes can be connected.

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.

A catheter body control system is provided that includes a first and a second robotic module and a manipulator device. The first robotic module is configured to couple with a guide catheter manipulator device to control advancement or angulation of the guide catheter within a heart chamber or to couple with a first control dial of a handle of a TEE probe to control advancement or angulation of a flexible shaft of a TEE probe within an esophagus. The second robotic module is configured to couple with a device catheter angulator to alter an angle of a tip of the device catheter within a heart chamber during a procedure or to couple with a second control dial of a handle of a TEE probe to control advancement or angulation of the flexible shaft of the TEE probe. The manipulator device is configured to advance a tip of the device catheter or TEE probe relative to anatomy of the patient.

The invention relates to the technical field of manipulator control, in particular to a livestock grabbing manipulator control method and system. The method comprises the following steps: carrying out stress state collection and struggling force monitoring on livestock in a grabbing state of a manipulator to obtain livestock stress state struggling force monitoring data; according to the livestock stress state struggling force monitoring data, whole body subcutaneous tissue gap shrinkage difference quantification is carried out, livestock needle tube injection fracture risk estimation is carried out, and livestock needle tube injection fracture risk data is obtained; manipulator injection behavior regulation and control are conducted according to the livestock needle tube injection fracture risk data, and manipulator injection behavior regulation and control data are obtained; and manipulator automatic control firmware design is carried out based on the manipulator injection behavior regulation and control data, and manipulator automatic control firmware is obtained. The manipulator control technology is optimized, so that the manipulator control technology is more accurate.