494 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 relates to the technical field of manipulator control, in particular to a multi-dimensional force touch sense self-adaptive grabbing dexterous hand control system and a multi-dimensional force touch sense self-adaptive grabbing dexterous hand control method. The control system comprises an upper computer, a dexterous hand master controller module, a finger controller module, a three-dimensional force tactile sensor module and an actuator module, wherein the finger controller module, the three-dimensional force tactile sensor module and the actuator module are independently configured for each finger of the dexterous hand; wherein the dexterous hand master controller receives an instruction of an upper computer and generates a control instruction corresponding to each finger; the three-dimensional force tactile sensor monitors contact data of a finger and an object; the finger controller calculates target positions of the servo motor at different time points based on the control instruction, the contact data and actuator state feedback, and generates a third control instruction; and after receiving the instruction, the actuator module adjusts the position of a servo motor to ensure that the dexterous hand completes self-adaptive grabbing according to a preset track and force. The grabbing force and speed of the dexterous hand can be adjusted in a self-adaptive mode according to different conditions, and the accuracy and stability of the grabbing action are ensured.

The invention discloses a humanoid robot step terrain walking method based on a whole-body kinetic model, and the method comprises the steps: designing a nonlinear model prediction control problem oriented to the walking of a humanoid robot, and reasonably planning a system state, input and a reference track of a foot end; equality constraints and inequality constraints which are necessary for walking of the robot are designed, and the constraint that a landing point of the robot needs to fall in a landing area is taken as one of the inequality constraints, so that the humanoid robot is capable of walking on a step terrain; and finally, solving a nonlinear model predictive control problem in real time by utilizing a solver, and issuing a robot joint torque according to an inverse kinetic equation of the whole-body kinetic model and PD in combination with a feedforward controller, so that the robot is better in control effect and more stable in walking.

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 relates to the field of fiber jumping manipulators, in particular to a fiber jumping manipulator control method and device, equipment and a medium. The method comprises the following steps of: quickly responding to a wiring request, automatically calculating an optimal path and quickly and accurately finishing the steps of grabbing, moving, inserting and the like of the optical fiber patch cord by automatically controlling the patch cord manipulator, so that the wiring time is greatly shortened, the labor intensity of workers is reduced, the labor cost in patch cord operation is reduced, and the production efficiency is improved. And the overall working efficiency is improved. And automatic control can also realize real-time monitoring and recording of the wiring process, so that subsequent operation and maintenance management and troubleshooting are facilitated. By recording and analyzing the wiring process, the wiring path and the operation method can be continuously optimized, and the operation and maintenance management level is further 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 present invention relates to a robot system, comprising: - a robot body (11); - a manipulator (10); - a control unit (20); and - at least one laser scanner (12); wherein the manipulator is mounted to the robot body; wherein the at least one laser scanner is mounted to the robot body; wherein the control unit is configured to move the manipulator relative to the robot body and / or control activation of the manipulator; wherein the control unit is configured to define at least one areal protective zone (14) at a periphery of the robot body, and wherein each areal protective zone of the at least one areal protective zone is defined at an angle between 45-90 degrees to the horizontal; wherein the control unit is configured to control the at least one laser scanner to project laser radiation in at least one 2D plane that is coplanar with the at least one areal protective zone and acquire at least one sensor data for reflected laser radiation within at least one coverage range or scan plane (13) within the at least one 2D plane, wherein each 2D plane is coplanar with an areal protective zone, and wherein each laser scanner is configured to project laser radiation in a 2D plane coplanar with an areal protective zone and acquire sensor data for reflected laser radiation within a coverage range or scan plane within the 2D plane that is at least a significant proportion of the areal protective zone; and wherein the control unit is configured to trigger a safety response upon acquisition of sensor data for reflected laser radiation within the at least one coverage range or scan plane within the at least one 2D plane.

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.

A robotic control system may be configured to translate a first set of location coordinates based on a first location numbering system to a second set of location coordinates based on a unified location numbering system. A robotic control system may receive layout data of a warehouse, the layout data containing a plurality of location coordinates of racks and storage locations. The location coordinates may be of a first format based on the first location numbering system that is specific to the warehouse. The robotic control system may analyze the format of the location coordinates to select, from a plurality of candidate conversion algorithms, a suitable conversion algorithm to translate the plurality of location coordinates of the first format to a second format based on the unified numbering system. The robotic control system may store the translated location coordinates for use in generating a topometric map of the warehouse.

The invention discloses a bionic manipulator control method, system and device based on sEMG signals and a storage medium, and belongs to the technical field of bionic manipulators. The method comprises the steps that collected multi-modal signals are processed through a constructed multi-modal feature fusion neural network so as to recognize grabbing actions; the identification tag of the grabbing action is converted into a control signal of the bionic manipulator through a BP neural network; the multi-modal feature fusion neural network comprises an input layer, a feature extraction layer, a time sequence modeling layer, a feature fusion layer and an output layer; the feature extraction layer extracts local time sequence features of multi-modal signals, and the time sequence modeling layer models time sequence dependence of grabbing actions; a time-based attention mechanism is introduced into the feature fusion layer, the feature weight of each mode is adjusted in a self-adaptive mode, and then the features of the multiple modes are fused into a global feature vector. The recognition precision of the grabbing action can be improved, and therefore the control precision of the bionic manipulator is improved.

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

The invention discloses 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.

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 present disclosure relates to a computer-implemented method of synthesising a training dataset for training a comparison function of a machine learning service in the detection of a multi-pick event in which a robotic manipulator erroneously picks two or more items concurrently, the method comprising: selecting first image data representative of an image of a plurality of items to be picked by the robotic manipulator; selecting intermediate image data representative of an image of the plurality of items following the removal, by the robotic manipulator, of an item from the plurality of items; selecting final image data representative of an image of the plurality of items following the removal, by the robotic manipulator, of another item from the plurality of items; and, generating the training dataset representative of a notional multi-pick event based on a sequence of unlabelled image data consisting of the first and final image data.

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.