95 results about "Robot manipulator" patented technology

A computer-implemented method, apparatus and system is provided for robotic task completion from natural language requests. The method may include: receiving a natural language command, processing the natural language command with a generative large language model to extract an intent and associated context, creating a three-dimensional (3D) open-vocabulary semantic scene graph of the environment, associating the scene graph with the intent and associated context, creating, based at least in part on the scene graph, an execution plan comprising a sequence of actions to complete the natural language command, and generating executable code or tool calls corresponding to one or more actions in the sequence of actions, and controlling one or more robotic manipulators and / or actuators to perform one or more actions of the sequence of actions based on the execution plan.

A method of programming an industrial robot, comprising: recording movements of a robot manipulator during a demonstration-based programming session, to obtain a robot trajectory (301); using a natural-language model (171), parsing speech data describing the programming session into at least one robot parameter (305) value and annotating the robot trajectory with the robot parameter value; and, on the basis of the annotated robot trajectory (306), generating a robot program (307). The method further comprises: capturing a video (302) of the programming session; using a vision-enabled language model, VLM (172), generating descriptions (303) of movements of the robot manipulator which are visible in the video; and generating said speech data (304) on the basis of the descriptions of the robot manipulator movements.

A robot manipulator has a base; an arm connected to the base via at least a shoulder joint; and a control system with a dynamic stability determination module. The dynamic stability determination module of the control system determines whether a dynamic stability criterion is met. If the dynamic stability criterion is met, the arm is responds compliantly to an applied force; and if the dynamic stability criterion is not met, the control system causes motion of the base in the direction of the applied force.

Described herein are sample collectors configured for use with robotic medical systems. A robotic medical system can include a medical instrument that can be inserted into a patient to capture a sample. A robotic manipulator can be engaged with the medical instrument and configured to operate the medical instrument; a sterile barrier can be used to separate a sterile field containing the medical instrument from a non-sterile field containing the robotic manipulator; a sample collector can include a receiver portion positioned in the sterile field and configured to receive the sample when a distal end of the medical instrument is retracted from the patient; and a connector coupled to the receiver portion and configured to attach to the robotic medical system. The robotic system can place the sample in the receiver portion.

A robotic system includes an autonomous order picker lift truck configured to move within a commercial environment that includes commercial products supported by donor pallets; a robotic manipulator assembly positioned on a platform of the autonomous order picker lift truck; and a control system communicably coupled to the robotic manipulator assembly and the autonomous order picker lift truck. The control system is configured to perform operations including commanding the autonomous order picker lift truck to move to a particular donor pallet or shelf that is mounted on a rack assembly at or above a support surface of the commercial environment; commanding the autonomous order picker lift truck to move the platform to the particular donor pallet or shelf; and commanding the robotic manipulator assembly to pick a commercial product or a case from the particular donor pallet or shelf and place it on an order pallet or a receiving pallet.

The invention discloses an engineering machinery vehicle driving robot special operation manipulator which comprises a box body, an electric cylinder, a two-degree-of-freedom parallel five-connecting-rod mechanism and an operation handle clamping device, the electric cylinder is placed in the box body, two input ends of the two-degree-of-freedom parallel five-connecting-rod mechanism are respectively connected with the electric cylinder, and the two input ends of the two-degree-of-freedom parallel five-connecting-rod mechanism are connected with the operation handle clamping device. The operation handle clamping device is arranged outside the box body and connected with the output end of the two-degree-of-freedom parallel five-connecting-rod mechanism, and the operation handle clamping device can be connected with an operation handle of the engineering machinery vehicle to complete operation. The system can replace human drivers to complete high-risk operation tasks under the dangerous operation working conditions of engineering machinery vehicles such as bulldozers, loaders and mine clearance vehicles and multiple fields such as mines, nuclear power and emergency rescue, the risk of operators is reduced, the quick response capability is improved, and the working efficiency is improved. The technical bottleneck that unmanned operation is achieved on the premise that existing engineering equipment does not need to be changed is broken through.

To improve performance of input to a robot manipulator by a user.SOLUTION: The sensor unit is designed to record an input variable applied to the robotic manipulator by the user manually guiding the robotic manipulator, wherein the input variable is a kinematic variable or a force and / or a moment. The sensor unit is designed to transmit the input variables to the computing unit, and the computing unit is designed to convert the input variables by means of a predetermined input variable mapping, wherein the input variable mapping defines a mathematical mapping of the input variables to coordinates of the graphical user interface or to settings of the virtual control elements.SELECTED DRAWING: Figure 1

Surgical systems and methods involving machine learning models to characterize tool interactions. A tool may be coupled to the robotic manipulator and configured to manipulate tissue. The sensing system measures a displacement of the tool, a speed of the tool, and an interaction force applied to the tool. A control system controls the manipulator to move the tool to interact with the tissue. In response to interaction of the tool with tissue over time, the control system obtains measurements of displacement, speed, and interaction force from the sensing system. The control system implements a machine-learned model for receiving and processing the measurements to estimate stiffness / damping parameters of the tissue, and evaluating variations in the estimated stiffness / damping parameters to generate a characterization regarding the tissue or tool interaction with the tissue. The control system controls the manipulator and / or the tool based on the characterization.

This system increases the number of processes that can be automatically annotated, saving labor, and also detects feature areas in images that are not normally detected during processing. [Solution] A robot control device is used, comprising: a coordinate calculation unit that calculates coordinates from data of an object and a robot manipulator in a simulation environment; a first learning model generation unit that generates a first learning model including data of a first estimated position of the object and the robot manipulator in the simulation environment using first teaching data including coordinates on a simulation image; a second learning model generation unit that generates a second learning model including data of a second estimated position of the object and the robot manipulator in real space based on second teaching data generated using data of the object and the robot manipulator in real space; and a robot control unit that controls the operation of the robot manipulator based on the data of the second estimated position.

The invention provides a robot manipulator control method, device and equipment and a medium, and relates to the technical field of manipulator control, the method comprises the steps that according to obtained stress data of two hands of a robot, a trained neural network model is adopted, and the mass center position of a carried object is determined; on the basis of the double-hand force-moment balance condition, according to the mass center position, the grabbing point position of the carried object grabbed by the double hands of the robot is determined, and the double hands of the robot are controlled to grab the carried object; on the basis of a model prediction control technology and a control obstacle function technology, according to the obtained obstacle information, a collision-free translational motion track of the carried object and collision-free translational motion tracks of the two hands of the robot are generated; and according to the obtained translation acceleration when the two hands of the robot carry the carried object, the expected posture when the two hands of the robot carry the carried object is determined. According to the method, through logic connection among the steps, the defects of object grabbing, two-hand posture adjustment and obstacle avoidance links in the prior art are comprehensively overcome.

A remnant extraction tool includes a nosepiece assembly and a vacuum assembly. The nosepiece assembly engages with a robotic manipulator to adjustably align with an exit side of a workpiece during a drilling operation through the workpiece at a predetermined location. The drilling operation includes drilling performed by another robotic manipulator from a drilling side of the workpiece. The vacuum assembly is in fluidic communication with the nosepiece assembly and engages with the robotic manipulator to selectively apply a vacuum to the exit side of the workpiece via the nosepiece assembly and to collect remnants from the drilling operation. A robotic manufacturing system includes a machine drill tool and the remnant extraction tool. The machine drill tool engages with a first robotic manipulator. The remnant extraction tool engages with a second robotic manipulator. Methods for extracting remnants during drilling operations at robotic manufacturing systems are also disclosed.

A computer-implemented method comprises receiving a first configuration 108 of design parameters for a component of a robotic manipulator, wherein the design parameters comprise one or more discrete h

Surgical systems and methods involve a robotic manipulator configured to support and move a surgical tool, and a control system coupled to the robotic manipulator. The control system obtains a bone mineral density (BMD) map of a target bone and controls the robotic manipulator to move the surgical tool into contact with the bone at one or more points. Using the BMD map, the control system determines predicted BMD values of the target bone at the contact points. The control system calculates an estimated operational parameter of the surgical tool based on the predicted BMD values. The control system measures an actual operational parameter of the tool during interaction with the bone and compares it to the estimated operational parameter to identify any discrepancy. Upon detecting a discrepancy, the control system generates a corrective action or alert.

A robotic manipulator is provided that exhibits improved grip strength and dexterity. The manipulator includes multiple fingers, each of which includes a grip surface formed by a belt running along a. compliant member. Actuation of the fingers about axes of rotation that are not parallel to the grip surfaces can result in exertion of forces from the compliant member into an object, resulting in deformation of the compliant member, This deformation can improve the contact area and grip strength with which the object is held. The belt can be driven along the grip surface, allowing the object to also be rotated around an axis that is not parallel to the direction along the grip surface while still being held securely. Each finger may also include a compliant joint to allow the fingers to rotate passively when an object is held, orienting the grip surfaces toward the object to improve grip strength.

A method and an apparatus for controlling a robot including a manipulator is provided. The method may include: acquiring an image of a scene including a target object; inputting the image into a control policy model to obtain an object placement pose of the manipulator, as an output of the control policy model, wherein the control policy model is generated using data collected by a data collection system that is configured to acquire an object retrieval trajectory by observing a robot movement for object retrieval, and reverse the object retrieval trajectory into an object placement trajectory; and commanding the robot to position the manipulator according to the object placement pose, to place the target object at a designated location.

The present invention relates to a force control system and method for robotic manipulators. The method comprises: initializing (102) the position (u) of a virtual guide (6) on a predetermined spline (5); obtaining (104) a position and orientation of an end effector (4); updating (108) the position (u) and speed (I) of the virtual guide (6) on the spline (5) based on the projection of the vector in the direction tangent (d t ) to the spline (5) from the virtual guide (6) to the end effector (4); obtaining (110) a reference system centered on the virtual guide (6) with one tangential axis (eT) and two normal axes (eN1, eN2) to the spline (5); calculating (112) a distance in rotation (DR) from the orientation of the end effector (4) to the orientation of the virtual guide (6); calculating (114) a distance in translation (d N 1, d N 2) on the normal axes from the end effector (4) to the virtual guide (6); calculating (116) a Cartesian impedance force in rotation (II) and a Cartesian impedance force in translation (III) on the normal axes (eN1, eN2) using the distance in rotation (DR) and the distance in translation (d N 1 d N 2); and applying (118) the forces to the robotic manipulator (1).

1. Name of the designed product: robot manipulator. 2. Use of the designed product: the designed product is used for operating a robot. 3. Designing points of the designed product: in shape. 4. Picture or photograph best indicating the designing points: perspective view 1. 5. Other circumstances or explanations: in perspective view 1, the plurality of inclined parallel fine lines shown above the plurality of operation keys are lines for indicating the plane of the front side of the transparent plate, and are not lines for indicating a pattern or the like provided on the transparent plate. In each figure, the fine solid lines (thin lines) shown on the surface portion are all for indicating the shape of the perspective surface. The hatched portions in the reference figures indicate the transparent portions of the transparent plate. Perspective view 1 is a perspective view as viewed from the front right lower side, and perspective view 2 is a perspective view as viewed from the back right upper side.

A robot may include a manipulator arm including a plurality of links and one or more grippers configured to perform a gripper work. A camera may be positioned at an end of the manipulator arm to provide a direct line of sight to the grippers and observe the gripper work from above the grippers.

Methods of producing an incised tissue are presented. The methods may include: contacting a portion of a tissue grasping implement of a first robotic manipulator with a tissue to produce contacted tissue, grasping the contacted tissue using the tissue grasping implement of the first robotic manipulator to produce grasped tissue, and creating an incision in the grasped tissue using a surgical cutting implement of a second robotic manipulator to produce the incised tissue. Additional methods as well as related systems and computer readable media are also presented.

The application provides a robot mechanical arm cooperative control method based on imitation learning. The method comprises the following steps: constructing a robot historical state data set, performing feature extraction on the robot historical state data set through an encoder structure to obtain a latent feature representation; obtaining a color image and a depth image of a target operated by a robot to be predicted through a binocular camera, performing feature extraction processing on the color image and the depth image through a mode classification module and a visual feature extraction network to obtain a mode feature and a visual feature vector; inputting joint state information, the latent representation, the operation mode feature and the fused visual feature into a decoder to obtain an action block prediction result, performing weighted averaging on action blocks at multiple continuous time points based on a time series integration weighting mechanism to obtain a continuous control trajectory of the robot mechanical arm. The application solves the multi-stage strategy discontinuity and error accumulation problems existing in the traditional control algorithm, and realizes smooth action, stable task and double-arm cooperative control.

Systems, methods, and computer-readable storage media for gaze-guided control of a robot is provided. An example method includes receiving, from a headset, data associated with a gaze of a wearer of the headset. The method includes generating at least one motion profile based at least in part on the data associated with the gaze of the wearer and a gaze filter selection. The method further includes generating at least one command by applying an inverse kinematics algorithm to the at least one motion profile. The method further includes transmitting the at least one command to at least one manipulator of the gaze-controlled robot, where the at least one command triggers the at least one manipulator to move based at least in part on the at least one command.

The application discloses a kind of robot joint motor synchronous control systems, belong to the field of robot control system, including motor synchronous control unit, vibration active suppression module, wear evaluation and coping module and prediction module;Motor synchronous control unit is used to generate the synchronous drive instruction of robot joint motor, to realize the coordinated motion of multiple joints;Vibration active suppression module is used to suppress the elastic vibration of robot manipulator simultaneously when calculating synchronous drive instruction;Wear evaluation and coping module is used to capture vibration state as system internal state by state observer, and the wear degree of each joint of manipulator is evaluated based on vibration-wear model;Prediction module is used to predict the wear degree of each joint of manipulator based on vibration-wear model and historical record.The application can improve the vibration suppression effect, and provide wear evaluation and prediction function, to further improve the motion accuracy, stability and service life of robot.

The utility model relates to the technical field of automatic feeding equipment, in particular to a visual guidance manipulator capable of holding, clamping, overturning and pouring materials, which comprises a hollow box body with an open upper part and an open lower part, and a connector connected with a robot is arranged on the rear side of the box body. Clamping air cylinders with the output ends located in the box body are arranged on the two sides of the connector correspondingly, an anti-falling assembly is arranged at any one of the two corners of the rear face of the box body, a visual camera and a suction cup are arranged on the side face of the box body, a telescopic assembly is arranged between the visual camera and the box body, a fixing plate is arranged on the box body, and the suction cup is arranged on the fixing plate. An opening is formed in the middle of the fixing plate, ejection air cylinders are arranged on the front side and the rear side of the opening, and an inverted discharging hopper is arranged on the opening. According to the utility model, unmanned operation of monocrystalline silicon raw material feeding is realized, floating dust in a workshop is reduced, and material taking, material pouring and empty box recovery are completed at one time by the articulated arm robot manipulator.

The invention relates to the technical field of robot manipulators, and particularly discloses a robot manipulator based on deep learning, which comprises a track base, a six-axis robot, a mounting frame, a clamping part, a swing separation part and a control box, a moving part is arranged on the track base, the six-axis robot is arranged on the track base and performs linear motion through the moving part, and the swing separation part is arranged on the mounting frame. The output end of the six-axis robot is fixedly connected with the mounting frame, the clamping component is arranged on one side of the mounting frame and used for clamping fruits, and the swing separating component is arranged outside the mounting frame and used for driving the clamping component to swing in multiple directions. Fruit stems of the fruits are separated from the fruit trees. According to the invention, the swing separation part drives fruit stems to be smoothly separated from fruit trees through multi-direction swing, traditional pulling and shearing type picking is replaced, branches and fruits are prevented from being damaged, and the picking success rate is increased.

According to the present invention, use is made of a robot control device comprising: a coordinate calculating unit that calculates coordinates on a simulated image from data of an object and a robot manipulator in a simulation environment; a first learning model generating unit that uses first teaching data including the coordinates on the simulated image to generate a first learning model including data of first estimated positions of the object and the robot manipulator in the simulation environment; a second learning model generating unit that generates a second learning model including data of second estimated positions of the object and the robot manipulator in real space on the basis of the first learning model, the weights of the first learning model, and second teaching data generated by predetermined processing using data of the object and the robot manipulator in real space; and a robot control unit that controls the operations of the robot manipulator on the basis of the data of the second estimated positions. As a result, the number of processes that can be automatically annotated can be increased, saving labor, and characteristic parts of an image that are necessary for processing can be detected, even if said characteristic parts cannot be detected in automatic annotation.

The application discloses a kind of dual-axis optical fiber transmission engineering type terahertz time-domain spectroscopy systems, including terahertz host computer, transmission cable, terahertz detection module, detection sample and robot;Terahertz detection module is fixed in the end of robot manipulator, and terahertz host computer is connected with terahertz detection module by transmission cable 2;By the output of terahertz host computer polarization direction perpendicular to each other pump light and probe light is incident into terahertz detection module and excitation generates terahertz time-domain pulse, and terahertz time-domain pulse passes through detection sample, and after carrying the phase information of detection sample, it is reflected to terahertz detection module and generates induced current, and is returned to terahertz host computer by transmission cable, and terahertz host computer carries out detection path simulation to detection sample, adjusts robot detection posture and carries out scanning according to planning path, and completes in situ terahertz detection of detection sample.The application can be flexibly applied to a variety of engineering field detection environment.

This invention provides an adaptor for use with one or more robotic manipulators. In one embodiment, said adaptor comprises: a) a bowl (101) for attachment to a carrier system; b) an overtube (102) connected to said bowl (101), comprising at least one channel for insertion of said one or more robotic manipulators; c) a plurality of controls (118), each control (118) is adapted to control diameter of one of said at least one channel; wherein said at least one channel comprises two or more inlets (107) converging into a same channel; and each of said two or more inlets (107) is positioned on opposite sides on said bowl (101) or overtube (102).

A robotic manipulator includes a sensing device with a tactile interface featuring visual markers and a camera for capturing images of these markers. The system includes one or more processors and memories storing instructions which, when executed, direct the tactile interface to contact a target surface, deforming the visual markers. The camera captures visual feedback of this deformation, which is input into a machine learning algorithm. Based on the algorithm's output, the system controls the operation of a manipulator device. This configuration enables adaptive manipulation by interpreting tactile and visual data through machine learning.

A robotic system is configured to automatically identify surgical instruments used during a bronchoscopy procedure. The robotic system can include a video capture device, a robotic manipulator, sensors configured to detect a configuration of the robotic manipulator, and control circuitry communicatively coupled to the robotic manipulator. The control circuitry is configured to perform, using a machine learning classifier, a first analysis of a bronchoscopy video of a patient site to track a medical instrument in the bronchoscopy video. The control circuitry can then identify a set of possible instrument identifications for the medical instrument in the bronchoscopy video based on the first analysis and an identified phase of the bronchoscopy procedure. The control circuitry can then track a motion of the medical instrument in the bronchoscopy video and select an identification from the set of possible instrument identification for the medical instrument based at least on the tracked motion.