829 results about "Robot motion" patented technology

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention relates to the technical field of robot control, and discloses a heavy-load robot motion trail method and system based on machine learning. The method comprises the steps that historical movement track data of the heavy-load robot in a working scene are collected, and the data comprise a joint position sequence, an end effector pose sequence and environment obstacle distribution information; the data is preprocessed, track features are extracted, a space-time correlation matrix is constructed, and the matrix is used for representing the dynamic coupling relation between joint movement and the tail end pose; training a trajectory prediction model containing a long and short-term memory network and an attention mechanism based on the matrix, and generating a collaborative mapping relation between a joint position and a tail end pose; obtaining a current task target pose sequence and an environment constraint condition in real time, and outputting a candidate track set meeting dynamic constraint through a model; and adopting a multi-objective optimization algorithm to screen candidate tracks, generating an optimal track instruction and issuing the optimal track instruction to an execution mechanism. The method adapts to the complex characteristics and variable working conditions of the heavy-load robot, and the track adaptability is improved.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a joint denoising method for robot visual motion prediction, and the method comprises the steps: constructing a unified generative model through fusing an image and a depth map collected by a depth camera, motion data collected by CAN line communication of a Piper mechanical arm, and a tactile image collected by a Gelsight Mini tactile sensor; the method comprises two steps of data acquisition and input coding, and joint denoising and generation: firstly, multi-modal data are coded into low-dimensional potential representation, and then future images, depth maps, tactile data and robot actions are cooperatively predicted through a joint denoising framework based on Transform. A mask self-attention mechanism is innovatively introduced, information interaction between modes is dynamically adjusted, action generation is guided through tactile feedback, and the force control precision is improved. The model adopts a de-noising diffusion probability loss function to jointly optimize multi-modal prediction, so that the output consistency is ensured. According to the method, the robustness and the accuracy of flexible operation of the robot are remarkably improved.

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

The invention discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

The invention provides a robot control method and device, equipment and a storage medium, and relates to the technical field of robot control. The method comprises the steps of obtaining robot data of a robot executing a task, and determining a learning data set according to the data; building a first robot action prediction model based on the initial visual language model; obtaining a second robot action prediction model according to the data set and the first robot action prediction model; a robot time sequence, image data and a language instruction are taken and input into a second robot action prediction model, the time sequence data are input into a second language model through a preset time sequence model and a multi-layer projector, the image data are input into the second language model through a visual encoder and the projector, and the language instruction is input into the second language model through the first language model; the second language model outputs continuous actions to be executed; and completing the task according to continuous action control. And the time sequence model captures time sequence characteristics, so that the second robot action prediction model outputs continuous actions to be executed, and the operation smoothness and continuity are effectively guaranteed.

The embodiment of the invention provides a robot control method and related equipment. The robot control method specifically comprises the steps that target action information used for controlling robot movement is generated based on a robot control task; performing security verification on the target action information based on the motion security constraint information of the robot; wherein the motion safety constraint information is used for constraining the motion range of the robot in the space from at least one spatial dimension; under the condition that the security verification is not passed, optimizing the target action information until the optimized target action information passes the security verification; and driving the robot to move by using the target action information passing the safety verification. According to the invention, unsafe robot behaviors can be reduced.

This patent discloses a computer system for humanoid robot control system design and implementation, featuring a digital humanoid robot with dynamical models and a set of single-input-single-output (SISO) and multi-input-multi-output (MIMO) controllers. The system comprises a main software program, a generative Al humanoid robot intelligence engine, a robot motion path planner module, and a control system simulation engine. It enables efficient design, testing, validation, and implementation of robot control systems, significantly reducing time to market. The system supports seamless upgrades to accommodate new designs and components, enhancing applications in industrial automation, healthcare, public safety, and more, aligning with the goals of the 4th Industrial Revolution.

The invention provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.

The invention discloses an intelligent sorting and scheduling method and system for a multimodal transport scene, and relates to the technical field of warehousing, and the method comprises the steps: collecting the transportation mode identifiers of a plurality of transportation carriers, the physical attribute information of corresponding goods, and the operation state information of a three-dimensional sorting system in real time; analyzing the multimodal transport rigid rule information to generate a dynamic boundary condition of multimodal transport; importing the transportation mode identifier, the physical attribute information, the operation state information and the dynamic boundary condition into a deep reinforcement learning model; outputting a goods shelf distribution strategy for dynamically distributing storage levels according to the goods gravity center parameters and an equipment task sequence for controlling the AGV carrying robot to move; and a sorting execution instruction is automatically generated so as to correspondingly drive the three-dimensional sorting system to carry out goods sorting. According to the method, a'secure storage-time coupling-space cooperation 'technical closed loop is formed, and the comprehensive logistics cost of a multimodal transport full link is remarkably reduced.

The invention discloses a biped robot gait generation method based on enhanced confrontation motion prior, and belongs to the technical field of robot control. Comprising the following steps: constructing an enhanced PPO-AMP network; establishing an action network model, an evaluation network model and an AMP module; establishing a biped robot motion model, and selecting a simulation experiment environment; performing reinforcement learning on the enhanced PPO-AMP network by using a reward function combining task rewards and style rewards and combining an improved PPO-Clip mechanism with KL divergence constraint on the basis of a simulation experiment environment to obtain optimized network parameters; the optimized enhanced PPO-AMP network is used for generating the biped robot gait meeting the actual walking task. According to the control method, the environment perception capability is optimized, the network structure is deepened, and the reward mechanism and the training strategy are improved, so that the biped robot has high robustness and high natural gaits on various complex terrains at the same time.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention provides a robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

The invention relates to a welding pose track generation method of a workpiece fillet weld, a medium and equipment. The method comprises the following steps: acquiring a two-dimensional workpiece weld point; performing three-dimensional reconstruction on the two-dimensional workpiece welding seam points to obtain a three-dimensional workpiece welding seam point cloud; a plurality of geometric equations in the three-dimensional workpiece welding seam point cloud are extracted, track regeneration is conducted according to the geometric equations, and an initial welding track and key welding seam point coordinates are obtained; and attitude mapping is conducted according to the prior welding attitude and the key welding seam point coordinates, interpolation calculation is conducted according to the initial welding track, and a final welding pose track is obtained. The problems that in the prior art, the actual space pose of a workpiece cannot be sensed online, the three-dimensional contour of a fillet weld cannot be automatically recognized, and the motion trail of a robot cannot be generated in real time are solved.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

The invention relates to the technical field of robot control, and discloses a robot motion control method, device, equipment and medium, and the method comprises the steps: constructing a simulation environment and configuring a robot model, obtaining a depth image of an area in front of the robot model through a depth sensor, carrying out the semantic segmentation of the depth image, generating a semantic mask of a passable area, and carrying out the recognition of the passable area. And generating three-dimensional terrain point cloud data, extracting a semantic feature vector of the point cloud data, inputting the semantic feature vector and the body state information of the robot model into the motion control strategy model to generate a joint control instruction, and driving the robot model to move according to the joint control instruction. According to the method, depth image perception, semantic segmentation, three-dimensional point cloud reconstruction and feature extraction are fused, robot body state information is combined, a self-adaptive joint control instruction is generated, dynamic perception, decision making and control of robot motion in various complex terrains can be achieved, and the autonomous motion energy of the robot in an unstructured environment is effectively improved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention discloses an industrial robot motion state intelligent control method based on a twinborn model, and the method comprises the steps: collecting the motion state parameters, such as the joint angular velocity, the joint angular displacement, the position and posture of an end effector, of a robot through a multi-domain coupling twinborn modeling engine; transmitting to an industrial internet twinborn intelligent control center for classified storage, and establishing a parameter association mapping table; constructing a multi-domain coupling twinborn model based on the parameters and the mapping table, inputting motion state representation data output by the model into a deep enhanced evolutionary control algorithm with spatio-temporal attention enhancement, and generating multiple groups of control instruction candidate sets after spatio-temporal dimension features are extracted; and the center evaluates and screens the candidate set, selects an instruction meeting the requirement and transmits the instruction to a robot execution mechanism. The method depends on a space-time attention enhancement algorithm to improve the control precision so as to complete a control link to avoid a process fault, the intelligence and reliability of robot motion control are effectively improved, and the method is suitable for robot motion regulation and control under complex industrial working conditions.

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 relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention relates to the field of surgical robots, and particularly discloses an intraoperative risk assessment system for a cataract surgical robot, comprising: a patient biomechanical monitoring unit for collecting a contact force between an instrument and an eye tissue, and calculating an anterior chamber equivalent pressure value according to an axial force component of the contact force and a contact area between the instrument and a cornea; the patient optical imaging monitoring unit is used for collecting microstructure images of the cornea and the lens capsule and quantifying the energy release density of ultrasonic emulsification; the robot motion parameter monitoring unit is used for collecting joint motion resistance, instrument tail end three-dimensional coordinates, motion speed and attitude angle of the robot; the multi-mode joint reasoning unit is used for identifying a high-risk operation state when the contact force is higher than a first preset value and the energy release density is higher than a second preset value; by adopting the technical scheme of the invention, multi-dimensional real-time monitoring can be realized, and a dynamic risk prediction model can be established by fusing heterogeneous data through an intelligent algorithm.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention relates to a humanoid robot indoor action planning and action control method and system and a robot. The method comprises the steps that an environment image is collected and analyzed; the large model is adopted to convert original text execution input by a user into a structured command; constructing an open set scene graph; after the target description text is received, if the place is a strange place, the no-graph task planning module generates a humanoid robot action instruction according to the target description text and the environment image; if the scene is a long-term activity place, the graph task planning module adopts LLM for processing according to the open set scene graph and the task instruction, and outputs an action instruction sequence which is executed in sequence; according to the robot action instruction or the action instruction sequence, a corresponding action control strategy is called, and physical movement of the humanoid robot is controlled. According to the method, autonomous action planning and control of the humanoid robot in an indoor environment are realized by utilizing strong zero sample target recognition and semantic understanding reasoning capabilities based on image and language basic models.

A method for learning the gait imitation of a humanoid robot combined with periodic rewards relates to the field of robot motion control technology. In view of the problem of unstable posture of humanoid robots when walking in a humanoid posture in a plane in the prior art, this application constructs a reference action library that integrates contact information as a reference for imitation reward items and periodic contact reward items. This application creates a comprehensive reference action library for basic actions and their corresponding periodic contact information. This strategy introduces periodic reward items by imitating the style of the reference action and its contact information, which not only improves the realism and style consistency of the robot's actions, but also enhances the attention to the details of the interaction between the feet and the ground during the execution of the action, thereby ensuring the stability of the posture of the humanoid robot when walking in a humanoid posture in a plane.

The application relates to a multi-arm mobile robot control system, which comprises a robot, each joint position of the mechanical arm of which is configured with a joint controller for driving joint movement; a robot controller for realizing the operation of joint motors according to control quantities; a joint position hard analysis unit for calculating the coordinate positions of each joint or mechanical arm in a space coordinate system to output results; and a joint space point position trajectory constraint model for forming a correction quantity of a joint and superimposing the correction quantity on an original control quantity of the joint to become a final control quantity for controlling the joint, the final control quantity being used for output to the joint controller to realize joint movement. The application provides a multi-arm mobile robot control system, which effectively realizes the cooperative control among multi-arm robots, avoids the mutual collision among mechanical arms, improves the avoidance and dynamic response speed of the mechanical arms, and improves the agility of robot actions.

A robot-based assembly control method includes providing a plurality of neural network models comprising at least a reinforcement learning network model and a tensor fusion network model. Training data for the models includes visual data from the vision device, tactile data from the tactile sensor, motion feedback data from the robot and torque feedback data from the robot. An assembly control system based on the robot, which includes a clamping effector at an end of a movable part of the robot, wherein a tactile sensor and a soft rubber pad are provided on an inside clamping portion of the clamping effector from inside to outside is described. Based on fusing the visual information with the external forces of other dimensions indirectly obtained from changes in tactile signals, representation vectors can be used to generate appropriate instructions of robot motion for flexibly adjusting the insertion force to complete the assembly.

The invention provides a method and a device for predicting an operation track of an action-free labeling robot based on body flow representation, and the method comprises the steps: taking an initial point position in the body flow of the robot, a text feature in a language task instruction and a visual feature in an initial image as multi-modal condition features; the multi-modal condition features are input into the robot trajectory prediction model to obtain body flow information, and the robot trajectory prediction model can comprehensively consider motion of the robot, object related information contained in a language instruction and visual features in an initial image, so that more accurate body flow information is generated; the robot can accurately interact with a specific object; according to the method, the motion constraint information of each joint of the robot is obtained, the body flow information is decomposed into the motion component of each joint through forward kinematics mapping based on the motion constraint information of each joint, and kinematics constraints of different joints of the robot are fully considered, so that the accurate control of the robot motion is realized.