623 results about "Robot motion" patented technology

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 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 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.

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 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 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 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 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.

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 discloses a robot motion path real-time deviation rectifying method based on digital twinning, which comprises the following steps that an off-line programming system generates an initial welding track according to a welding requirement, converts the initial welding track into a code file, transmits the code file to a digital twinning system, and generates a preset track Q; the virtual model layer synchronously maps a motion path of a welding robot entity to obtain a real-time mapping track P; track points of the two tracks are matched, and wrong matching is eliminated; a robot track model is constructed, a predicted track T of entity motion of the welding robot is obtained, and track point matching is conducted on the predicted track T and a preset track Q; and the deviation value of the matched track points of the two tracks is calculated, the robot electric control system corrects the welding track of the welding robot entity according to the deviation value, and the welding track is corrected in real time. According to the correction method, track points are matched only by means of a digital twin system for real-time correction; the adopted track point matching algorithm is small in error and high in matching precision.

The invention provides a motion control method and device based on reinforcement learning, electronic equipment and a storage medium, and relates to the technical field of robot motion control. The motion control method based on reinforcement learning comprises the following steps: training and generating a teacher network model corresponding to a robot according to a robot model description file, road surface detection sample data and motion performance information, and determining student network training initial parameters corresponding to the teacher network model; training and generating a corresponding student network model according to the student network training initial parameters and the road surface detection sample data; deploying the student network model to the robot, analyzing a real-time image collected by the robot based on the road surface type classification detection network model, and determining a corresponding road surface type; and determining the target joint position offset of the robot based on the road surface type corresponding to the real-time image, the current road surface detection data and the student network model, and determining the joint torque of the robot according to the target joint position offset. The effect of controlling robot motion based on reinforcement learning is improved.

The invention discloses a robot obstacle avoidance method and device for a dynamic unstructured environment, equipment and a medium, is applied to a target robot, and relates to the technical field of computers, and the method comprises the steps: carrying out the feature fusion of collected multi-modal environment data of the dynamic unstructured environment, so as to obtain an environment perception data set; analyzing the environment perception data set through a pre-training semantic model to generate an environment evaluation report corresponding to the environment perception data set; generating an initial path reaching the target point based on the current pose information of the target robot and the environment evaluation report, and performing obstacle avoidance optimization on the initial path through the environment evaluation report to obtain an optimized path; and the optimized path is converted into a robot motion instruction, and a target point is reached by executing the robot motion instruction. Therefore, synergy of semantic-level obstacle understanding and path optimization in a dynamic unstructured environment can be realized, and the scene adaptation limitation of the traditional technology is broken through.

The invention relates to the technical field of robot motion control and trajectory planning, in particular to a robot motion trajectory planning method based on AI reinforcement learning. The method comprises the following steps: acquiring robot workspace layout information and an environment sensing probe candidate list, performing probe configuration planning, calibration and planning session index generation, and generating a planning session primary key recording structure; executing multi-source observation acquisition scheduling, data alignment and three-dimensional grid modeling, and generating a reinforcement learning state basic structure; state vector construction and continuous action strategy inference are carried out, and a candidate motion track set structure is generated; and track rolling simulation, grid collision risk assessment, multi-target reward calculation, target track selection and strategy parameter updating are carried out to generate a strategy network parameter set structure. Through combination of multi-source perception and reinforcement learning, intelligent planning of the motion trail of the robot is realized, the safety and efficiency of the trail are improved, and the adaptability and robustness in a dynamic environment are enhanced.

The invention provides a three-stage training method and device for a robot action strategy model and a storage medium, and the method comprises the steps: obtaining multi-modal training data in a first training stage, and obtaining a universal multi-modal data encoder through training according to the multi-modal training data; in the second training stage, a cross-body action strategy model is obtained according to the general multi-mode data encoder and the cross-body mechanical arm operation data; and in the third training stage, according to the mechanical arm operation data of the target configuration, fine tuning training is conducted on the cross-body action strategy model, and a target action strategy model for the target configuration is obtained. According to the method, the contradiction between the universality and the success rate of the target task can be solved, and the final success rate and the robustness on the specific target task are improved to the maximum extent, so that the basic task execution capability and the generalization capability of the model are greatly enhanced on the multi-configuration robot tasks, and the task execution efficiency is improved. And the optimal balance of universality, generalization ability and final success rate is realized.

The invention discloses an ultra-low splashing robot welding system and method based on intelligent waveform control, and relates to the technical field of robot welding, and the method comprises the following steps: collecting historical welding data, extracting steady state molten pool characteristic parameters, and constructing an associated database; establishing a three-dimensional coordinate system, and setting and verifying a molten pool monitoring area; molten pool images and temperature field distribution are collected in real time, dynamic molten pool characteristic parameters are extracted, the deviation degree between the dynamic molten pool characteristic parameters and steady-state characteristics is calculated, and whether waveform adjustment is triggered or not is judged by combining arc signals; a reference waveform parameter is called according to the current working condition, the waveform adjustment amount is output through a neural network, an intelligent welding waveform instruction is generated, and a welding power source and a robot are controlled to move through a real-time communication link; and monitoring the state and the splashing amount of the molten pool after adjustment, evaluating the splashing inhibition rate, and optimizing the system. Intelligent closed-loop control in the welding process is achieved, splashing is remarkably reduced, the welding stability and quality are improved, and the method is suitable for various automatic welding scenes.

The invention belongs to the technical field related to robot motion control, and provides a robot motion control method and system in a microgravity environment in order to solve the problems of stability and sand blowing of robot motion under existing microgravity. A robot swing stage is divided into three sub-stage motions, and a robot foot end expected track is generated for each sub-stage motion; the robot state information, the historical track and the environment information are input into a pre-constructed joint control model, and a joint control instruction of the robot in the microgravity environment is obtained through prediction; the joint control model is a strategy network model trained in a reinforcement learning mode; the trajectory tracking reward is determined by the actual position of the robot and the expected position of the foot end of the robot. Stable, low-disturbance and high-efficiency movement of the robot in the microgravity environment is achieved, and meanwhile the sand blowing phenomenon is effectively restrained.

The invention discloses a double-arm robot main and auxiliary moving operation coordination control method based on vision / force sense servo and implementation thereof. Double-arm coordination is supported, multi-sensing information is fused, compliant operation is considered, and the method is adaptive to a moving scene. The mobile main and auxiliary double-arm robot control system comprises a mobile base subsystem, a main and auxiliary double-arm subsystem and a control subsystem. The double-arm main and auxiliary operation coordination control method based on visual / force sense servo comprises visual servo target tracking based on a tail end camera and force sense servo compliant operation control based on a force sensor, operation tasks are achieved in the state that a base moves, and the double-arm main and auxiliary operation coordination control method adapts to the complex working environment; a state machine mechanism is adopted to manage a mechanical arm motion mode, screw assembly task-oriented operation process arrangement is realized, and the efficiency and controllability of the operation process are improved.

The invention provides a robot action arrangement method and device, and relates to the technical field of robots. The method comprises the following steps: acquiring a first action sequence and a second action sequence, and determining a first action missing interval between the first action sequence and the second action sequence; inputting the first motion missing interval and the second motion missing interval into a pre-trained motion transition model to obtain a first transition motion sequence corresponding to the first motion missing interval; and sequentially splicing the first action sequence, the first transition action sequence and the second action sequence into an arrangement action sequence. According to the method and the device, smoother and adaptive connection among different actions can be realized, and the robot is ensured to have higher performability in action arrangement.

The invention relates to a quadruped robot motion control method based on visual fault tolerance, and the method comprises the steps: building a simulation environment according to the motion control demands of a robot; defining a blind estimator and a visual estimator, organizing a training environment according to scene requirements, and training a switching strategy between the blind estimator and the visual estimator; determining the motion constraint of the robot based on a cMDP constraint Markov decision process, and solving a robot motion control strategy in combination with the trained blind estimator and visual estimator; and correspondingly controlling the motion state of the robot according to the robot motion control strategy. Compared with the prior art, the advantages of visual estimation and ontology perception are combined, the robot can move more flexibly and stably in the complex terrain, a switching strategy between estimators is designed, the visual fault-tolerant capability can be enhanced, the motion stability of the robot is guaranteed, in addition, an end-to-end one-stage training framework is adopted, and the training efficiency is improved. The training and deployment efficiency can be improved.

The invention discloses an intelligent path optimization control system for an industrial robot. The intelligent path optimization control system comprises a task input and analysis module, an environment sensing module, an intelligent modeling and path planning module, a feedback and execution module and a robot execution unit, the task input and analysis module sends a task instruction to the environment sensing module; the environment sensing module collects environment data, forms a dynamic semantic map and sends the dynamic semantic map to the intelligent modeling and path planning module. The intelligent modeling and path planning module sends a robot joint track instruction to the feedback and execution module; the robot motion unit drives the robot to execute trajectory tracking according to a motion control instruction issued by the feedback and execution module, and meanwhile, the feedback and execution module is connected with the weight coefficient input end of the intelligent modeling and path planning module to dynamically optimize the weight coefficient. Robot path planning is automatically adjusted to avoid collision, and it is ensured that the robot efficiently completes tasks.

The invention relates to the technical field of medical robots, in particular to a surgical robot and a remote motion center dynamic adjustment method for the surgical robot, and the method comprises the steps: building a kinematics constraint model between a tool axis of a surgical tool and a robot base coordinate system; on the basis of a visual sensor and a hand-eye transformation matrix, a pose measurement value of the remote motion center point under a robot base coordinate system is obtained in real time; fusing a pose state prediction quantity driven by a robot motion instruction and the pose measurement value, and generating an optimal estimation pose and a state covariance matrix of the remote motion center point; according to the state covariance matrix, determining whether the remote motion center point has a large offset; when it is judged that the large-amplitude offset occurs, a safety strategy is triggered to control the surgical robot. According to the technical scheme, dynamic tracking and real-time adjustment of the remote motion center point in the operation are achieved, and the problems that a traditional scheme is insufficient in flexibility and poor in safety are solved.

The invention provides a quadruped robot wet-slippery rugged terrain motion control method based on explicit and implicit estimation, and aims to solve the problem that the motion ability of a quadruped robot under the wet-slippery rugged terrain is limited due to the fact that the existing method cannot fully extract environment and robot motion state information. Historical observation information and a current action are utilized to explicitly estimate an environment friction factor, a four-foot contact state and a robot centroid velocity, and current motion model information of the robot is implicitly estimated through a method of predicting a motion state at a next moment. Environmental information, robot motion information and implicit vectors containing robot motion model information are introduced into input of a motion strategy, higher sensing capacity for the motion state and the environment is provided for a motion network, and a novel control method suitable for the quadruped robot to move in the slippery terrain is obtained.

Motion is coordinated between a robot and a mover in an independent cart system. Positions for a mover along a track for the independent cart system and for each axis of motion of the robot mounted on the mover are received at the motion controller. The axis position corresponds to either an angular position of the motor or a mechanical position of a corresponding axis on which the motor is mounted. The motion controller receives a robot motion command corresponding to a desired operation of the robot and generates a mover motion command for the mover and an axis motion command for each axis of the robot. The mover motion command and the axis motion commands are generated as a function of the robot motion command, the position of the mover along the track, and the position of the motor or of the axis for each axis of the robot.

The invention provides an inspection robot trajectory planning method suitable for a compact obstacle environment of a power distribution room, and solves the problems that keys are compact and an obstacle is close to a target key in the power distribution room environment. According to the method, a bee colony and a Q table are initialized, a fitness function is constructed, robot motion and environment constraints are introduced, and an exploration or development strategy is dynamically selected according to a population state. A particle swarm algorithm with control parameters is introduced in a scout bee stage, and the global search capability and convergence efficiency are enhanced. The optimal solution is updated through an elitist strategy and neighborhood search, and the Q value is adaptively adjusted by means of feedback, so that the strategy flexibility is improved. According to the method, the global search advantage of the particle swarm and the local development advantage of the artificial bee colony are integrated, the trajectory precision and the operation safety are guaranteed, the efficiency and the real-time performance are also improved, the success rate reaches 98.16%, the time is reduced by 10.11% compared with that of an artificial bee colony algorithm, and the method is suitable for popularization and application. The method is suitable for a patrol robot track planning task with a complex distribution room structure, a compact control panel arrangement and a closer obstacle to a target.

The invention provides a robot action prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting multiple pieces of visual information of a robot and task execution instruction information into a first network layer of a pre-trained robot action prediction model for key point prediction processing, outputting key point features of the operated object grabbed by the robot; wherein different visual information comes from different mechanical arms of the robot; and performing action prediction processing on current mechanical arm state information, multiple pieces of visual information, task execution instruction information and the key point features based on a second network layer of the robot action prediction model, and outputting an action sequence of the robot. The key point features of the clamping jaw on the mechanical arm of the robot and the operated object are accurately determined through the robot action prediction model, and then action prediction processing is conducted through the key point features to improve the accuracy of robot action sequence determination.

The invention discloses an automatic laying and forming process for a reinforcing ring of a carbon fiber fully-wound gas cylinder end socket, and relates to the technical field of carbon fiber wound gas cylinders. Technological parameters are simulated and optimized; the laying line type is imported into software to plan the motion track of the robot; carbon fiber prepreg is adopted, single-layer prepreg laying is conducted between every two spiral layers of the gas cylinder according to the optimal process parameters, and the laminating state of the prepreg is monitored in real time. Full-automatic precise laying can be achieved, and the production efficiency and consistency are improved; the material utilization rate is improved; the manufacturing cost is reduced; the laying wrinkle defect is eliminated, and the gas cylinder bearing safety is improved; process parameters are quantified and optimized, and the performance of the carbon fibers is fully released.