2455 results about "Robotic systems" patented technology

Systems and methods that allow robots to perform tasks for users are provided. A robot may comprise one or more robotic arms and / or a mobile base. The arms may be controlled by electric actuators and may have six or more degrees of freedom. The robot may have sensors which can be accessed remotely. Robots may have varying levels of autonomy, including, for example, full teleoperation (in which a human can have detailed control over the robot) or full autonomy (in which the robot can complete a task without any human intervention). Various entities can interact with individual robots or groups of robots over a network, locally, directly or in person, or any combination thereof. A management system can allow entities to control and / or monitor the robots.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention discloses a six-degree-of-freedom hybrid robot system based on error identification and compensation, and belongs to the technical field of robots and automatic equipment. The system comprises a static platform, a movable platform, three parallel branch chain assemblies, a parallel robot support, a three-degree-of-freedom series rotary joint assembly, an end effector assembly and a controller module. The static platform is of an equilateral triangle structure, the parallel robot support is installed above the static platform, and the three parallel branch chain assemblies are arranged above the static platform at equal intervals through the parallel robot support. The movable platform is mounted on the three parallel branch chain assemblies to form a 3-PTT parallel mechanism; the parallel mechanism provides three-degree-of-freedom translational motion; the three-degree-of-freedom series-connection rotary joint assembly is hinged to the movable platform, and the three-degree-of-freedom series-connection rotary joint assembly is a 3R series-connection mechanism composed of three direct drive motors, a base connecting base, a middle support and a terminal connecting base and used for achieving three-dimensional posture adjustment of the end effector assembly; the end effector assembly is used for executing tasks; and the controller module is used for realizing coordinated control of a parallel mechanism and a series mechanism. Through the modular 3-PTT + 3R series-parallel topological design, decoupling and accurate connection of translation and rotation freedom degrees are achieved, and the three-degree-of-freedom parallel connection mechanism has high rigidity, high maintainability and excellent geometric consistency.

The invention discloses an intelligent accompanying robot system, and the system comprises the following modules: a multi-mode interaction module which is composed of a voice recognition unit, a voice synthesis unit, an emotion recognition unit, and a visual recognition unit, and is used for collecting the voice, facial expression, motion, and environment information of a user; the localized AI decision module is used for deploying a lightweight DeepSeekR1 large language model based on an ESP32-S3 edge computing chip, performing real-time processing on the multi-modal data and generating a social guidance strategy and an emotion intervention instruction; the data management module comprises a user behavior database and a privacy protection unit and is used for desensitizing the data and realizing sensitive data isolation through local storage; according to the method, the single-person intervention cost is reduced, and the time consumption of manual scene simulation is reduced.

The invention provides a humanoid robot hand-eye coordination intelligent calibration control system and a humanoid robot hand-eye coordination intelligent calibration control method, and the humanoid robot hand-eye coordination intelligent calibration control system integrates high-precision contour extraction, visual feedback closed-loop control, multi-sensor fusion and dynamic path planning. The robot hand-eye calibration method has the beneficial effects that the calibration efficiency and precision can be remarkably improved, the problem of calibration parameter drift caused by system time-varying characteristics and environmental interference is effectively solved through continuous tracking of a target contour and real-time online compensation of pose deviation, and the calibration accuracy is improved. Therefore, the reliability and the absolute positioning precision of the robot in long-term complex operation tasks are guaranteed, and the self-adaptive capability of a robot system is enhanced.

The present disclosure relates generally to robotic systems, and more specifically to systems and methods for specifying a robot task configuration by means of annotating a visual workspace representation in coordination with a waypoint optimization process. This system enables a much broader use of robotic automation by saving time and reducing technical complexity. This system can be used to configure any visually enabled robotic task, such as tasks in warehouse management, manufacturing, delivery, inspection, logistics, etc.

The invention provides a binocular vision teleoperation robot system, a robot and a teleoperation method and device, and relates to the technical field of computers. The method comprises the following steps: receiving a binocular video stream obtained by carrying out image acquisition on an actual scene by a binocular vision robot, and rendering the binocular video stream to obtain a three-dimensional scene; displaying the three-dimensional scene to an operator of the binocular vision robot to capture motion data of the operator observing the three-dimensional scene; determining a target area from the actual scene according to the action data, and obtaining a visual control instruction based on the target area; the vision control instruction is transmitted to the binocular vision robot for teleoperation; wherein the visual control instruction is used for adjusting imaging parameters of the binocular vision robot for image acquisition of an actual scene, and the visual direction of the binocular vision robot can be rapidly and naturally kept consistent with the attention point of the operator.

In various examples, a technique for generating simulation data includes generating, via one or more simulations, simulation data associated with operation of a first machine in an environment. The technique also includes determining a command to the first machine based at least on the simulation data and a goal associated with the first machine and updating the simulation data based at least on the command. The technique further includes storing the simulation data, the command, and the updated simulation data in one or more data records, and causing a second machine to perform one or more actions based at least on the one or more data records.

The invention provides a robot environment sensing method, device and system based on multi-sensor fusion. The method disclosed by the embodiment of the invention can comprise the following steps: receiving original observation data of each sensor configured on a robot body, and obtaining equivalent observation data of each sensor under system global time based on a time drift model of each sensor and the original observation data of each sensor, health degree indexes of all the sensors are calculated, time-varying credibility weights of all the sensors are generated according to the health degree indexes, activated sensor subsets are determined according to the health degree indexes of all the sensors, and state vectors of the robot system are determined according to the time-varying credibility weights of all the sensors in the activated sensor subsets and the equivalent observation data; and generating environment representation data based on the state vector of the robot system, the time-varying credibility weight of each sensor and the equivalent observation data. According to the invention, the influence caused by the time synchronization error and the space registration deviation between the sensors can be effectively avoided, and the sensing precision is improved.

Surgical robotic systems and navigation systems employ a robotic arm including a plurality of links. A link tracker assembly is located on the surface of one or more of the links to enable the link to be tracked by a localizer. The link tracker assemblies can utilize a plurality of tracker elements that can be removably coupled to the link. A photosensor assembly is located on the surface of one or more of the links to receive signals from the localizer for controlling the link tracker assembly. The robotic arm can be coupled to a base and the systems can be used in collaboration with a base tracker that is coupled to the base. The robotic arm can also include a mounting interface for receiving an end effector and the systems can be used in collaboration with an end effector tracker that is coupled to the end effector.

The invention discloses a robot hand-eye calibration method, control equipment and a robot system, and relates to the technical field of robots. The robot hand-eye calibration method comprises the following steps: uniformly correcting a plurality of pixel coordinates obtained by dynamic shooting to a reference pixel coordinate system which is consistent with a camera coordinate system at a reference position in direction; and meanwhile, the world coordinates of the end effector corresponding to each sampling position are localized relative to the world coordinates of the end effector at the reference position, so that interference caused by global coordinate offset and camera orientation difference is eliminated, and the application range of the hand-eye calibration method is expanded. Besides, a singular value decomposition algorithm is adopted to solve a mapping relation between pixel coordinates and local world coordinates under a reference pixel coordinate system, so that a solved rotation matrix can meet orthogonality constraints, the hand-eye transformation process strictly conforms to a robot motion model with translation and rotation motion, and the calibration precision is improved. The calibration process can be automatically calculated and completed, and the calibration efficiency is high.

The invention discloses a pitaya robot system based on intelligent treatment and recuperation and a control method, and the system comprises a high-precision image collection and human body image collection and three-dimensional reconstruction module which is used for precisely recognizing and positioning a treatment region and acupoints; the core physician manipulation digitization module is used for finely capturing professional treatment manipulations and standardized recuperating manipulations of deep physicians for operating pitayas through a multi-dimensional sensor array and forming a manipulation model library; the intelligent strategy generation module based on artificial intelligence can call, fuse or optimize and generate an optimal execution scheme from a corresponding model library according to a mode selected by a user; and the high-precision and compliant force control robot execution module reproduces the digitized technique of the pitaya in a high-fidelity manner under the real-time closed-loop control of visual guidance and force sense feedback. Through the double-track parallel strategy, quantification, inheritance and popularization of the high-level pitaya therapy are achieved, and application of the high-level pitaya therapy is pushed to the wide field.

A method for training a controller to control a robotic system includes: receiving a neural network of an original controller for the robotic system based on origin data samples from an origin domain and labels in a label space, the neural network including encoder and classifier parameters, the neural network being trained to: map an input data sample from the origin domain to a feature vector in a feature space using the encoder parameters; and assign a label of the label space to the input data sample using the feature vector based on the classifier parameters; updating the encoder parameters to minimize a dissimilarity, in the feature space, between: origin feature vectors computed from the origin data samples; and target feature vectors computed from target data samples from a target domain; and updating the controller with the updated encoder parameters to control the robotic system in the target domain.

The invention discloses a robot system with tactile perception, and belongs to the technical field of tactile robots. The problem that an existing robot is poor in environment pressure sensing capacity is solved. Comprising a head module, an integrated vision-inertial navigation sensing module and a head controller, two side surfaces are provided with a pair of rotatable connecting ends, and the outer surface is provided with a tactile sensor; the head controller transmits data to the upper computer through the communication module; the driving body module comprises a plurality of modularized joints which are connected end to end, touch sensors are arranged on the two side faces of each modularized joint, and the adjacent modularized joints are orthogonally connected; the modularized joint is integrated with a joint controller and a joint steering engine; the tail module integrates a tail controller and a tail steering engine, and tactile sensors are arranged on the two side faces of the tail module. The tail controller carries out data transmission with the head controller through the adjacent previous joint controller in sequence, and the head controller receives a control instruction of the upper computer and carries out data transmission with all other controllers. According to the invention, the environmental pressure can be sensed in all directions.

The invention discloses an elderly accompanying robot system based on multi-mode emotion cognition reinforcement learning. The system comprises a robot body, a multi-source data acquisition and feature extraction unit, a multi-data-source dynamic feature fusion unit, an emotional state estimation and modeling unit and a personalized interaction strategy generation unit. The multi-source data acquisition and feature extraction unit is used for extracting voice signals, physiological signals and behavior signals; the multi-data-source dynamic feature fusion unit is used for realizing cross-modal coupling through a learnable attention mechanism; the emotional state estimation and modeling unit is used for constructing a three-dimensional emotional vector space, predicting an emotional state through a mode of fusing time sequence characteristics and reinforcement learning, and converting a multi-modal signal into a quantifiable emotional index; and the personalized interaction strategy generation unit is used for generating a personalized interaction strategy for controlling the robot body.

The invention provides a motion control method of a surgical robot and a surgical robot system. In accordance with an example of the present application, the surgical robotic system includes a manipulator assembly that is movable under actuation of an actuator; the method comprises the following steps: determining whether the manipulator assembly collides or not, wherein the collision comprises an imminent collision or an already-collided collision; in response to the collision of the manipulator assembly, adjusting a control parameter of the actuator to reduce a current output of the actuator; the manipulator assembly is actuated by the actuator to move. The motion state of the manipulator can be adjusted in time, and damage caused by collision is effectively reduced; meanwhile, adjustment can be carried out when it is detected that the manipulator assembly collides, the motion tracking performance of the manipulator assembly is effectively maintained without the collision risk, and the accuracy of the operation is improved.

The invention relates to the technical field of areca-nut picking, in particular to a flying robot system for areca-nut picking, and adopts the technical scheme that a full-autonomous areca-nut picking robot system is constructed by deeply fusing high-precision multi-source visual perception, intelligent decision planning and compliance control technologies; according to the system, a sensing scheme combining multispectral imaging and structured light three-dimensional measurement is adopted, so that the recognition rate, the positioning precision and the maturity discrimination reliability of areca nuts in a complex illumination and shielding environment are remarkably improved; an extended Kalman filtering and advanced control law-based flight attitude stabilization algorithm effectively inhibits external disturbance and provides necessary platform stability for accurate picking in dynamic flight; the picking executing mechanism with the lightweight design is combined with real-time force feedback control, lossless grabbing of fruits is achieved, and the fruit damage rate is greatly reduced; and due to an intelligent energy management and task planning strategy, the overall energy efficiency of the system is optimized, and the endurance time and the picking quantity of single operation are prolonged.

The invention discloses a sewage plant intelligent inspection robot system and method based on multi-modal perception, and the system employs a cloud-edge-end collaborative architecture, and comprises an intelligent inspection robot, an edge calculation unit and a cloud platform. The intelligent inspection robot integrates a visual perception unit, a voiceprint perception unit, an environment perception unit and a mechanical contact unit, and synchronously acquires multi-modal data; the edge calculation unit operates the lightweight model to carry out preliminary anomaly recognition; the cloud platform realizes equipment high-confidence diagnosis, residual life prediction and maintenance work order generation by combining SCADA / DCS real-time process data through a feature level fusion diagnosis model based on a cross-modal attention mechanism; the robot has self-adaptive inspection capability and can respond to real-time abnormal events to adjust paths. According to the invention, the transformation of sewage plant equipment operation and maintenance from passive maintenance to active prediction is realized, and the intelligent inspection level and the operation and maintenance efficiency are remarkably improved.

The Portable Digital Twin Robotic System based on Computer Vision for the Identification, Tracking, and Destruction of Suicide Drones is an innovative system that autonomously identifies, tracks, and destroys suicide drones by leveraging Digital Twin and computer vision technology. This system analyzes visual data using artificial intelligence algorithms and performs precise positioning in real-time. Aided by the Digital Twin, the precise simulation of the system's dynamics and movement enables the optimization of paths and precise control of the robot, while the real-time communication between the digital model and the physical robot provides the capability for immediate updates of control algorithms and predictive maintenance. The portable design and the capability to be connected to various types of weapons make this system suitable for installation on military and non-military platforms and improves the security of sensitive areas.

The invention relates to a method for monitoring a robot system comprising a robot arm and a peripheral device, the method comprising the steps of: providing a communicative peripheral connection between the peripheral device and a robot controller; establishing an operation signal history in a digital storage wherein the operation signal history is based on operation representations; executing a robot operation process on the robot controller; establishing a peripheral signal associated with the peripheral connection; recording the peripheral signal to obtain a peripheral signal representation; updating the operation signal history by providing the peripheral signal representation as an operation representation of the operation representations; and tracking operation of the robot system based on the operation signal history. The invention further relates to a robot system.

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, for operation of an assembly robotic system. In one aspect of the disclosure, the assembly robotic system includes a tool coupled to a robot device and configured to be selectively coupled to a first object. The assembly robotic system also includes a welding tool, one or more sensors configured to generate sensor data, and a controller. The controller is configured to control the tool to couple the tool to the first object based on the sensor data, control the robot device to bring the first object into a spatial relationship with a second object, and generate a weld instruction to cause the weld tool to weld a seam formed between the first and second objects. Other aspects and features are also claimed and described.

In one embodiment, a method includes receiving a natural language instruction from an interface device, receiving sensor data of a physical environment from a sensing system, converting the natural language instruction into instruction tokens and the sensor data into sensor tokens. The method further includes executing an autoregressive generative model on the instruction tokens and sensor tokens to output control tokens. The method further includes generating control commands based on the control tokens and transmitting the control commands to a robotic system. The control commands cause the robotic system to perform an action affecting the physical environment.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention discloses a double-arm robot real-time cooperative control system based on constraint optimization. The system is composed of a double-arm collaborative optimization layer and a safety control layer, task coordination and obstacle avoidance are combined, and collaborative control of a double-arm robot under the condition that safety constraint conditions are met is achieved. According to the method, accurate cooperation and obstacle avoidance control of two arms in a complex environment can be realized, and the method is suitable for a multi-task and multi-constraint robot cooperation control scene. Meanwhile, the method has good real-time performance, and an efficient and safe control strategy is provided for the double-arm robot system.

The invention relates to the technical field of intelligent education and Internet of Things fusion, in particular to a multi-modal interaction control method and system of a multifunctional teaching-assistant robot and the robot. According to the system, a tablet AI processor runs an Android system as a core, a display module, a man-machine interaction module, an audio input module, an audio output module, an image acquisition module, a temperature and humidity sensor module, a WIFI or Bluetooth module, an Ethernet module and an Internet of Things module are connected, and the Internet of Things module supports RS485 wired access and Zigbee wireless access at the same time; the tablet AI processor executes voice interaction, video call, face recognition, environment monitoring, network interaction and equipment linkage, and provides a face recognition alignment and depth feature matching algorithm and an annular microphone array beam forming and sound source direction estimation algorithm, so that multi-modal interaction and multi-equipment linkage in a teaching scene are realized. The convenience and the safety are improved; and the equipment access cost is reduced.

The invention provides an intelligent tax auditing robot system based on NLP and OCR technologies, and relates to the technical field of tax auditing. The data acquisition module collects enterprise tax related data. And the data processing module constructs an enterprise tax knowledge graph. And the risk assessment module generates a risk report and early warning information. And the intelligent interaction module explains the risk assessment result to obtain a decision suggestion. And the report generation module generates an enterprise tax audit report. And the learning optimization module performs real-time optimization and adjustment. According to the method, the dynamic tax rule engine is established through tax regulations and enterprise historical data, and the enterprise tax risk can be accurately identified in combination with the risk assessment model of machine learning. Through real-time comparison and deep analysis, abnormal conditions in the enterprise tax data can be found in time, the risk level is accurately evaluated, tax staff are helped to take measures in advance, enterprise tax risks are reduced, and tax violation behaviors are avoided.

The present disclosure provides a humanoid robot system comprising a mechanical structure with at least 30 degrees of freedom across torso, arms, and legs, actuators driving the degrees of freedom, sensors including cameras and proprioceptive sensors, and a computing system implementing a hierarchical bipedal action model (BAM). The BAM includes: a Delta model processing sensor data and user input to generate latent representations at a first frequency; a Gamma model receiving latent representations to generate human task actions at a higher second frequency; a Beta model translating task actions into joint configurations at a higher third frequency; and an Alpha model converting joint configurations into actuator control signals at a higher fourth frequency.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention relates to the technical field of artificial intelligence, intelligent and robot control, and discloses an emotion service robot system based on a multi-mode mental theory, which comprises a hierarchical mental reasoning module, a connection module and an action semantic execution module, the hierarchical mental reasoning module is used for generating a decision text containing a high-level strategy according to the multi-modal environment information collected by the robot; the connection module is used for constructing a semantic instruction according to the decision text generated by the hierarchical mental reasoning module; and the action semantic execution module is used for generating a control action of the robot according to the semantic instruction constructed by the connection module and the real-time image acquired by the robot. According to the method, a control architecture fusing a vision-language-action model and a robot center view angle mental reasoning hierarchy is constructed, so that the robot can carry out multi-order belief reasoning and implicit target inference from the view angle of the robot. According to the system, the robot is no longer a pure instruction follower and has an active service capability.

The application discloses a kind of high-precision spraying devices and methods of complex rotary body surface, device includes injection pump, six-axis robot, rotary table, precision spray gun and robot system, and complex rotary body workpiece is set on rotary table;Precision spray gun is set on six-axis robot, injection pump is used to inject coating to precision spray gun according to the set coating flow, and coating is sprayed to complex rotary body workpiece according to constant flow rate, spray width by precision spray gun;Feed line is connected between precision spray gun and injection pump;Through robot system, six-axis robot, rotary table, precision spray gun and injection pump are integrated control, so that the paint mist of precision spray gun is scanned with constant distance, speed and step all points on the surface of complex rotary body workpiece.This application can accurately control coating thickness, improve the resolution of coating, and also reduce environmental pollution during spraying.