61 results about "Robot trajectory" patented technology

This invention discloses a robot trajectory learning method and system based on surface dynamic motion primitives. The method includes: acquiring multiple teaching trajectories, parameterizing and rotating the trajectory sampling points to construct a reference coordinate system; learning trajectory features in the u and v directions based on the DMPs framework, and generating a static surface through Gaussian smoothing and weighted averaging; modeling obstacles using ellipsoids, designing dynamic obstacle avoidance coupling terms embedded in the DMPs formula to enable real-time bending of the surface for obstacle avoidance; learning velocity and acceleration information through classical DMPs, and combining it with parameter mapping of the starting / target position of the new task to generate an obstacle avoidance trajectory and control the robot's motion. This invention solves the problems of traditional DMPs being unable to directly learn surfaces, having lag in obstacle avoidance response, and losing spatial information, improving the robot's trajectory adaptability and real-time obstacle avoidance capability in semi-structured environments, and ensuring assembly accuracy and safety.

A surgical robot system includes a surgical robot, a robot arm connected to such surgical robot, and an end-effector connected to the robot arm. A registration fixture is used in conjunction with various registration systems in the surgical robot system. Such registration systems likewise include a detachable base in the form of a detachable dynamic reference base, along with an associated mount, the dynamic reference base and mount having certain features which permit the dynamic reference base to be selectively attached, detached, and reattached at different phases of an operation, whether pre-operative or intra-operative, and such successive attachments are done without the dynamic reference base, and tracking markers associated therewith, losing registration. Related methods allow for the more efficient and effective performance of operations by virtue of the dynamic reference base maintaining its registration during attachments and reattachments.

With the rapid development of technologies such as the Internet of Things (IoT), smart devices, the Industrial Internet, and the Internet of Vehicles (IoV), various systems collect, process, and store massive amounts of data during operation, including sensitive information such as process parameters of industrial equipment, robot trajectories, location information of vehicle systems, indoor images of smart homes, and trade secrets of enterprise office systems. If this private information is not properly protected, it may lead to serious consequences such as the leakage of trade secrets, infringement of user privacy, and legal compliance risks. Existing technologies suffer from insufficient proactive identification capabilities, simplistic protection strategies, lack of adaptive learning, and disconnected auditing. This application provides a privacy data management method, system, and computer-readable storage medium, aiming to achieve proactive identification, hierarchical protection, adaptive optimization, and end-to-end auditing of privacy information in various types of data. It can be widely applied to various data processing scenarios such as industry, vehicles, homes, and enterprises.

This invention provides a robot filament placement trajectory correction method based on model predictive control, relating to the field of filament placement control. The method includes the following steps: planning a filament placement reference trajectory using CAM software; the robot performing the first layer of filament placement according to the reference trajectory; collecting the first layer processing data during the first layer placement process; constructing a predictive controller based on the first layer processing data to compensate for trajectory errors during the filament placement process; obtaining the trajectory error using the first layer processing data to construct a trajectory error model; optimizing the parameters of the predictive controller using the least squares method; the parameters of the predictive controller including a disturbance term; performing an optimization calculation of the predictive controller parameters once in each control cycle; and applying the output control quantity of the predictive controller to the robot joint actuators to achieve compensation for the filament placement trajectory error. This invention achieves high-precision filament placement control.

The application relates to a kind of double-robot trajectory optimization methods for complex curved surface workpiece spraying task.The method comprises: establishing double-arm motion model, obtaining double-arm space point cloud distribution by Monte Carlo method, obtaining the geometric model of the spraying workpiece and the corresponding spraying key path point, and establishing the spatial pose relationship between robot end effector and workpiece;Based on the key path point, a segmented polynomial trajectory planning method is used to construct an initial spraying trajectory;The intermediate node speed parameter in the segmented polynomial trajectory is used as the optimization variable, and a multi-objective evaluation function containing trajectory length, curvature distribution, end attitude deviation and kinematic constraint is constructed;The evaluation function is solved by using a particle swarm optimization algorithm, and the optimal intermediate node speed parameter that meets the spraying process requirement is obtained;Based on the optimized intermediate node speed parameter, the final spraying trajectory is generated.The application can improve the system operation stability under the premise of ensuring the spraying quality, and has strong engineering application value.

The application provides a six-axis robot trajectory optimization method combining kinematics constraints and parameterized simulation verification, and relates to the technical field of robot trajectory planning, and comprises the following steps: determining a path segment to be optimized of a six-axis robot based on entity hardware composition of a workstation, signal control logic and process beat requirements; constructing a six-axis robot trajectory planning model; generating an optimized trajectory of an end effector of the six-axis robot by adopting a quintic polynomial interpolation algorithm; performing process point selection on the optimized trajectory of the end effector of the six-axis robot, and performing risk simulation verification on the selected process points by utilizing a mixed collision early warning mechanism of the separation axis theorem and a neural symbolic distance field, until the optimized trajectory of the end effector of the six-axis robot does not have an interference risk, and finally obtaining the end effector trajectory of the six-axis robot. The application can avoid hardware loss caused by equipment collision and raw material waste in the debugging process.

The application relates to a multi-robot cooperative simultaneous localization and mapping method in a wide range environment, belonging to the field of simultaneous localization and mapping, comprising initial pose calibration of a plurality of single-robot front ends, and generation of a key pose graph and a key frame point cloud under a wide range environment after initial pose calibration of each single-robot front end; loop detection is performed based on the key pose graph to find a loop candidate pair, according to a priority of the calculated loop candidate pair, two key frame point clouds corresponding to two nodes of the loop candidate pair are used, two-stage coarse-to-fine loop confirmation is used, and an accurate relative pose transformation between the two nodes is obtained as a loop detection result; and based on the key pose graph, the key frame point cloud and the loop detection result, key pose graph aggregation and optimization are performed to obtain a multi-robot trajectory and a global point cloud map. The problems of poor fault tolerance of single-robot mapping precision limitation and improvement of multi-robot SLAM robustness fault tolerance are solved.

The application provides a control operation method of a rare earth metal electrolytic furnace discharging robot, comprising the following steps: the discharging robot acquires the electrolysis state of the electrolytic furnace through a visual sensor and judges whether the discharging standard is reached; after judging that the discharging standard is reached, the discharging robot grasps the discharging tool, preheats the discharging tool and moves to a discharging position to prepare for discharging; the discharging robot moves along a planned path, monitors the contact force between the robot and the electrolytic furnace through a force perception system, corrects the robot track according to the contact force and executes the discharging process; after the discharging process is finished, it is judged whether the discharging is continued, and if not, the operation is ended. The application judges the molten metal discharging standard in real time through the visual sensor, dynamically calibrates the furnace mouth position and monitors the tool deformation, realizes the end compliance control through the force sensor, avoids the damage to the furnace body in the digging operation, reduces the manual participation and greatly improves the operation efficiency.

This application discloses a six-axis robotic arm trajectory planning method and system, relating to the field of robotics technology. The disclosed six-axis robotic arm trajectory planning method and system solves the problems of joint trajectory jumps, tool center point trajectory deviations, and velocity oscillations in traditional trajectory planning by discretizing the tool center point trajectory, constructing a binding inverse solution space, applying a multi-objective optimization algorithm to search for the optimal joint variable sequence, and performing double interpolation processing. It can avoid joint trajectory jumps, ensure the accuracy of the tool center point trajectory, and improve motion smoothness.

The application provides a robot trajectory generation method and system based on diffusion and rule inference, and relates to the technical field of autonomous navigation.The application proposes a novel conditional diffusion trajectory generation model based on architecture induction bias, which is not only dependent on loss function for result constraint, but also divides the complex trajectory generation task into two mutually coordinated technical processes:1, based on local environment graph construction, graph convolution propagation and graph level feature convergence, the inference of the overall passable structure and obstacle topological relationship within the scope of the current local environment graph is realized;2, based on node level scene representation, target feature continuous injection and prediction step by step context update, the fine-grained local maneuvering control of obstacle avoidance, turning and end convergence behavior at each future waypoint is realized, and the high-fidelity generation of the trajectory in the complex unstructured environment is ensured.Meanwhile, a GTGRU is designed, the end drift problem is solved by introducing time attention bias.

This invention provides a system, method, and computer program product for dynamic reconstruction of robot trajectories under large language model task interaction. It includes: constructing a global coordinate system point cloud based on sensor data, obtaining point clusters through spatial clustering, constructing a local geometric tensor matrix and calculating hardness coefficients to identify hard agglomeration regions; dividing the global point cloud into point cloud voxels, selecting task-related voxel sets, and calculating voxel physical resistance feature weights and comprehensive cost values. It plans an environmental reconstruction path from the robot to the agglomeration region, sets a breaking parameter sequence based on resistance weights, and drives the breaking mechanism. Based on torque data during the breaking process, it identifies the critical point of agglomeration breakage through time-domain feature analysis, calculates the environmental reverse action gain signal, and corrects the corresponding voxel resistance weights. Based on the corrected resistance weights, it again achieves dynamic trajectory reconstruction through a heuristic path search algorithm. This invention realizes intelligent, closed-loop, and adaptive robot trajectory planning.

The invention belongs to the technical field of airplane coating repairing, and particularly relates to an airplane coating precision repairing method based on 3D visual data, which comprises the following steps: shooting a workpiece needing coating repairing to obtain an RGB image, and converting the RGB image into a point cloud picture; after the point cloud picture is processed, a 3D model of a workpiece needing coating repairing is obtained, spraying distance parameters are set, a robot track is generated, path simulation is conducted on the track, if simulation is free of interference, an online laser scanner is arranged to run according to the robot track, secondary detection and positioning are conducted on the surface of the workpiece needing coating repairing, and then the workpiece needing coating repairing is obtained. Acquiring spatial position data of a to-be-restored area; the industrial personal computer generates a robot path middle point according to the spatial position data of the area needing to be sprayed and associates the robot path middle point to an I / O control instruction of the robot; spraying parameters are input into the robot, after a robot motion program is generated, the complete robot track is simulated again, and if simulation is free of interference, the complete robot track is imported into a robot controller to execute spraying.

The application provides a kind of six-propeller cooperative driving underwater robot control method, belongs to underwater robot motion control technical field.The method is based on the propulsion structure of rectangular configuration four-propeller and differential propeller, and the overall dynamics model of underwater robot is established in advance;The pose and motion state data obtained by the sensor are input into the controller, the desired attitude of the robot is calculated according to the given tracking trajectory, and the corresponding control quantity is further solved from the desired attitude;The control quantity is sent to the thrust distribution module, the mapping from control quantity to propeller driving signal is completed, the PWM pulse width command of each propeller is obtained and acts on the rectangular configuration four-propeller and differential propeller;Through real-time loop of the above steps, the cooperative control of robot trajectory tracking and attitude stability is realized.

This invention belongs to the field of trajectory planning for multiple types of vehicle robots, specifically a distributed trajectory planning method for vehicle robot swarms in complex dynamic environments. It includes the following steps: 1) Through fault-tolerant hybrid A... * The algorithm performs spatial reference path planning; 2) it constructs an S-T graph based on the generated reference paths and performs one-dimensional A... * The method involves: 1) Searching for and completing search-based velocity planning; 2) Performing spatiotemporal joint optimization of the reference path planning and velocity planning to generate a smooth, safe, and feasible trajectory that satisfies the nonholonomic constraints of the vehicle-like robot. This invention addresses the problems of high computational burden and lack of adaptability to dynamic environments in centralized mobile robot swarm frameworks by designing a distributed trajectory planning method for vehicle-like robot swarms. This method has broad application value and is significant for reducing system complexity and cost, improving system accuracy and real-time performance, and even expanding the application of vehicle-like robots in a wider range of applications.

The application discloses a robot trajectory learning generation and obstacle avoidance method and system, and belongs to the technical field of robot control and trajectory planning. The method firstly performs probability coding on robot teaching trajectory data through Gaussian mixture regression (GMR) to extract mean and covariance information; then a new type of kernel function is constructed to encapsulate the variability information obtained by GMR into the prior of Gaussian process (GP) to form a non-parametric GPGR model. According to the task requirement, a passing point is set, and the GP is modulated by using the derived necessary and sufficient condition of hard constraint to ensure that the generated trajectory passes through the preset passing point with a probability of 100%, and the accurate reproduction of the trajectory is realized. In addition, the obstacle information is obtained by combining a visual sensor, and the dynamic and efficient obstacle avoidance is realized by inserting the obstacle avoidance passing point in the model in real time. The application solves the problems of high calculation complexity of the traditional parametric method and the inability to realize hard constraint control in the prior art, and improves the accuracy and safety of trajectory planning of the robot in a complex environment.

Techniques are disclosed that enable model predictive control of a robot based on a latent dynamics model and a reward function. In many implementations, the latent space can be divided into a deterministic portion and stochastic portion, allowing the model to be utilized in generating more likely robot trajectories. Additional or alternative implementations include many reward functions, where each reward function corresponds to a different robot task.

The application provides a kind of six-propeller cooperative driving underwater robot control method, belong to underwater robot motion control technical field.The method is based on the propulsion structure of rectangular configuration four-propeller and differential propeller, and the overall dynamics model of underwater robot is established in advance;The pose and motion state data obtained by the sensor are input into the controller, the desired attitude of the robot is calculated according to the given tracking trajectory, and the corresponding control quantity is further solved from the desired attitude;The control quantity is sent to the thrust distribution module, the mapping from control quantity to propeller driving signal is completed, the PWM pulse width command of each propeller is obtained and acts on the rectangular configuration four-propeller and differential propeller;Through real-time loop of the above steps, the cooperative control of robot trajectory tracking and attitude stability is realized.

This invention provides a method and apparatus for measuring the thickness of steam turbine blades. The method includes: scanning the steam turbine blade according to preset scanning parameters to obtain first point cloud data; performing point cloud preprocessing on the first point cloud data to obtain second point cloud data; performing coarse registration processing on the second point cloud data to obtain third point cloud data; performing fine registration processing on the third point cloud data to obtain fourth point cloud data; performing trajectory conversion based on the fourth point cloud data to obtain robot trajectory data; and having a robot equipped with a detection component measure the thickness of the steam turbine blade according to the robot trajectory data to obtain the thickness measurement result of the steam turbine blade. This invention can improve the accuracy and efficiency of steam turbine blade thickness measurement.

The application discloses an underwater laser impact light guide arm and workpiece robot collaborative regulation system and method, and the method comprises the following steps: according to different shapes of workpieces to be machined, a work platform is built and adjusted, and a corresponding working environment is built in offline programming software; laser impact parameter setting is completed, a 3D visual dynamic scanning system is used, a double-robot trajectory is planned based on an A* algorithm and is sent to a terminal controller; the controller is connected with the double robot, a laser displacement sensor is started, and the system enters a standby state; the double robot is driven to point i according to the planned trajectory, a 3D visual feedback is performed on the relative pose of the light guide arm and the workpiece impact point and pose compensation is performed until the perpendicularity deviation is less than a threshold value; the laser displacement sensor feeds back the distance from the end to the workpiece and distance compensation is performed until the distance deviation is less than a threshold value, a laser pulse is triggered, and point i impact is completed; the above steps are repeated, and laser impact of the entire planned path is completed.

This disclosure relates to the field of biomimetic robotics, specifically providing a method and apparatus for generating robot motion trajectories. The method includes obtaining a corresponding behavior sequence based on the robot's motion behavior; for each time point in the behavior sequence, determining the optimal variable corresponding to each time point based on a target dynamics model; and determining the target trajectory corresponding to the motion behavior based on the optimal variable. In this disclosure, a target dynamics model for optimizing high-dynamic motion trajectories is constructed based on the robot's center-of-mass dynamics and whole-body kinematics. This simplifies the trajectory optimization algorithm and effectively reflects the whole-body motion information of the robot during high-dynamic motion. By encoding the corresponding behavior sequence for high-dynamic motion, the method accurately reflects the motion information during various high-dynamic motion processes, thereby improving trajectory planning accuracy.

The application discloses a kind of based on RBF neural network's magnetic adsorption welding robot trajectory tracking control method and system, and it is related to robot control technical field.The present application includes: by collecting robot operation full-dimension data and carrying out characteristic processing, trajectory characteristic region is divided using improved clustering algorithm, construct trajectory tracking control model based on RBF neural network, model training is completed in combination with optimized weight update rule, finally output accurate trajectory correction amount through real-time input operation state data, realize high-precision trajectory tracking control to magnetic adsorption welding robot.The present application effectively overcomes the influence of nonlinear time-varying factors such as magnetic adsorption force fluctuation, workpiece surface topography change, welding thermal interference, has the advantages of control logic simple, response speed fast, tracking precision high, robustness strong, adapts to various complex welding operation scene.

The application discloses an industrial robot trajectory planning method and a controller, and the method comprises the following steps: acquiring an industrial robot motion trajectory related parameter, determining trajectory starting information and a constraint condition; dividing a trajectory model of the robot and establishing an acceleration planning model according to the trajectory model; solving optimal parameters of the trajectory according to the constraint condition, and re-integrating an acceleration formula to obtain a trajectory position planning model. In the application, a segmented exponential trajectory planning method is adopted to plan the robot trajectory. The application has the advantages that by adjusting the proportion of each stage in the industrial robot trajectory planning, especially the proportion of the deceleration stage, the impact of the trajectory planning on the robot body in the whole motion process can be reduced.

The application discloses a rope-driven flexible robot based on neural network adaptive backstepping fault-tolerant control method, comprising the following steps: S1, obtaining a dynamic model and giving a desired reference trajectory; S2, introducing a quaternion to eliminate error and obtaining tracking error of a robot working end; S3, designing a backstepping fault-tolerant control law, according to a robot model and a control target of trajectory tracking, designing a control law of a robot trajectory tracking controller and determining control parameters; S4, introducing an RBF neural network model to approximate unknown quantities which are difficult to directly obtain in the control law; S5, analyzing stability and convergence of the backstepping fault-tolerant system, adjusting control parameters of the controller and outputting trajectory tracking results. The application designs an RBF neural network adaptive backstepping fault-tolerant controller for a rope-driven flexible continuous robot, on the basis of guaranteeing stable tracking of the system by using the backstepping control scheme, a fault-tolerant control scheme is added, so that safety of the robot is ensured when a fault occurs in an actuator.

The application discloses a motion trajectory optimization method in an industrial robot ultrasonic scanning, and relates to the technical field of robot trajectory optimization, and comprises the following steps: acquiring a discrete path point set of a workpiece surface, and dividing the path points into strict constraint points and smooth points according to the variation rate of the normal direction at each path point; calculating a node vector based on the discrete path point set, and then constructing a B-spline representation curve, a fitting error term and a smoothing penalty term; constructing an equality constraint matrix based on the strict constraint points; constructing and solving a KKT equation set to obtain an optimal solution of a control point, which is brought into the B-spline representation curve to obtain an optimized B-spline curve; uniformly sampling the optimized B-spline curve to generate a smooth trajectory point sequence; determining the normal direction of each point in the generated trajectory point sequence; and driving the robot to act according to the trajectory point sequence and the normal direction of each trajectory point, so as to solve the problems of unstable posture and low scanning quality in the traditional robot scanning process.

This application discloses a method and related products for weld seam extraction and robot trajectory generation based on digital model prior guidance, relating to the field of automated welding technology. The method includes: acquiring downsampled point clouds and undownsampled point clouds, where the downsampled point clouds are obtained by downsampling a coarsely registered point cloud, and the undownsampled point clouds are the coarsely registered point clouds without downsampling; finely registering the downsampled point clouds with the digital model point cloud to obtain a fine registration matrix; projecting the undownsampled point clouds onto the spatial coordinate system of the digital model point cloud using this matrix to obtain a projected point cloud; spatially clipping the projected point cloud using a predefined region of interest (ROI) for the weld seam in the digital model point cloud to obtain a weld seam region point cloud; performing local geometric feature analysis on the weld seam region point cloud, and extracting the weld seam trajectory based on a discrete set of weld seam feature points, and generating a smooth robot welding trajectory. This application can achieve high-precision, low-computing-cost weld seam trajectory generation.

This invention discloses a predefined-time sliding mode control method for a robotic arm trajectory tracking system. Most existing control methods rely on the initial state of the system and rarely consider the real-time performance of the robotic arm system and the ability to complete the task within a predefined time. The method of this invention includes: establishing a dynamic model of the robotic arm system; establishing a full-drive system model of the robotic arm system; designing a predefined-time sliding mode controller; designing a predefined-time sliding mode controller for the robotic arm system based on the full-drive system method; stability analysis of the system during the approach phase; and stability analysis of the system during the sliding phase. Based on predefined-time control theory, the full-drive system method, and sliding mode control theory, this invention considers external disturbances to the robotic arm system and allows the desired performance to be achieved within a given time. It designs a predefined-time sliding mode controller based on the full-drive system method, achieving effective control of the robotic arm system's trajectory tracking.

This invention relates to an automated assembly headlight bracket positioning and clamping mechanism and its assembly method. The mechanism includes a gripping fixture and an auxiliary non-standard fixture for an AGV (Automated Guided Vehicle) transport vehicle. The gripping fixture determines the position of the headlight bracket product by positioning the auxiliary non-standard fixture. A six-axis robot is mounted on a servo-controlled four-sided turntable, receiving visual information from the auxiliary non-standard fixture on the AGV transport vehicle and using the gripping fixture to grasp it. This mechanism enables continuous automated assembly production throughout the day. The auxiliary non-standard fixture is closely fitted to the headlight bracket product, avoiding relative displacement and ensuring installation accuracy. The gripping fixture determines the position of the headlight bracket product by positioning the auxiliary non-standard fixture, achieving Y and Z-axis positioning and clamping to ensure product stability. The six-axis robot's trajectory can be adjusted to mimic the force points and trajectory of manual installation, adjusting its posture to complete the installation.