19 results about "Robot vision systems" patented technology

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

The utility model discloses a kind of intelligent cutting and welding devices, including linear ground rail, handling robot is installed on the linear ground rail, gripper is installed on the mechanical arm of handling robot, workbench is provided at one side of linear ground rail, two arc ground rails are provided at the other side of linear ground rail.Affinity effect is in at:the utility model is through the cooperation design of arc ground rail, cutting robot, welding robot, linear ground rail, handling robot, vision system, three-jaw chuck and hydraulic jacking assembly, can when titanium sponge reactor bottom flange cutting and welding, realize the reasonable zoning of cutting, handling, welding, operation is convenient, can quickly realize the cutting and welding operation of titanium sponge reactor bottom flange, suitable for all titanium sponge reactor bottom flange cutting and welding occasion, save artificial and time cost, improve operation efficiency simultaneously, reduce security risk.

The application discloses a method and equipment for automatically generating a training data set in a mowing robot vision system, and the method comprises the following steps: S01, acquiring a scene condition control signal and generating a geometric constraint signal; S02, constructing a structured prompt word and inputting the same into a text condition of a text encoder to obtain text encoding; S03, inputting initial noise, the geometric constraint signal and the text encoding into a diffusion generation model, and converting the initial noise, the geometric constraint signal and the text encoding into a preliminary image data set through image decoding; S04, preliminarily screening the preliminary image data set according to semantic consistency and visual quality, performing instance segmentation on the preliminarily screened image data set to obtain an instance mask, and finely screening the instance mask according to a quality feature of the instance mask to obtain a finally generated training image data set. The application can automatically generate a high-quality visual training data set meeting training requirements without relying on real scene collection and manual labeling.

The utility model discloses a feeding and discharging device and a feeding and discharging system.The feeding and discharging device comprises a picking assembly, a mechanical arm, a robot vision system and a distance sensor, and the picking assembly is installed at the tail end of the mechanical arm; the robot vision system is installed on the mechanical arm and used for obtaining an image of the resistor disc and obtaining the first position of the resistor disc on the first plane according to the image of the resistor disc, the distance sensor is used for detecting the distance between the picking assembly and the resistor disc, and the mechanical arm drives the picking assembly to move according to the first position and the distance. And the resistor discs are accurately picked up. According to the feeding and discharging device, through cooperation of the mechanical arm, the picking assembly, the robot vision system and the distance sensor, automatic determination of the positions of the resistor discs and automatic taking and placing of the resistor discs are achieved, manual taking and placing of the resistor discs are replaced, the working time of workers in the high-voltage and noise environment of resistor disc production and machining is shortened, and the working efficiency is improved. And the harm of high-voltage and noise environments to workers is reduced.

The invention discloses a visual sensing information preprocessing method, system and device applied to a nuclear emergency robot and a medium, relates to the technical field of image processing, and solves the technical problem that the image quality of the nuclear emergency robot is poor. The image detail reduction capability in an extreme environment is effectively improved, the structural similarity of a dark area reaches 90% or above, and meanwhile, the irradiation resistance of a visual module is improved to a certain extent on the algorithm level. The framework can be applied to a nuclear emergency robot vision system and can be popularized to other emergency low-illumination radiation field scenes at the same time, and the problem that a traditional vision system is low in returned imaging quality is solved.

The invention discloses a coal-fired power plant denitration urea automatic unpacking and feeding system and a control method, and belongs to the technical field of coal-fired power plant denitration. The system comprises a material stacking station, an unstacking station, a bag breaking station and a waste bag recycling station, and through cooperation of an AGV forklift, an unstacking robot, a 3D vision system and a bag breaking machine, full-process automatic operation of urea bags from stacking tray recognition, transferring, unstacking, bag breaking, feeding to waste bag recycling is achieved. The control method is based on the system, and through laser SLAM navigation, visual identification and a distributed coordination algorithm, accurate linkage and safe operation of all devices are ensured. According to the invention, the manual workload and the labor intensity are effectively reduced, the working efficiency is improved, the flying dust pollution is avoided, the stability and the reliability of urea solution preparation in the denitration process are guaranteed, and remarkable economic benefits and environmental benefits are achieved.

The invention provides a honeycomb structure part core lattice defect detection method and system based on robot vision, and relates to the technical field of industrial detection. The method comprises the following steps: shooting a multi-view image of a to-be-detected honeycomb structure part by using a robot vision system subjected to hand-eye calibration, and storing pose parameters of the tail end of an industrial robot in the robot vision system when the image is shot; dividing an independent core grid region in each image by using an image instance segmentation model based on deep learning; utilizing a deep learning image classification model to generate a defect category label of each core grid region; and based on the obtained pose parameters, projecting the core lattice areas in all the images one by one, and judging and removing repeated core lattices in the projection process to obtain a three-dimensional visual core lattice defect detection result of the whole part. According to the invention, accurate identification, positioning and three-dimensional visual presentation of surface defects and bottom welding defects of the honeycomb structure are realized.

The present application belongs to the technical field of damaged image restoration, and particularly relates to a damaged image restoration method for a binocular vision system, which utilizes the time sequence characteristics of the current view image of the autonomous robot and the spatial information of the adjacent reference view image, models the time sequence information of the current view image based on the conditional generative adversarial network and the improved Transformer, and fuses the spatial information of the adjacent reference view image with the backbone network by using a relevant convolution layer to decode and reconstruct the restoration result; the present application can enable the autonomous robot equipped with the vision system to better cope with the unstructured working environment, ensure the integrity and accuracy of the acquired visual image information, and improve the accuracy and robustness of the robot vision system.

According to the cleaning method and system suitable for the robot dog and the robot vision system, an independent cleaning liquid supply module and an independent high-pressure spraying module are designed, and the cleaning liquid supply module is installed on a robot body of the robot dog or the robot in an embedded mode; the high-pressure spraying module is hidden in a machine body of the robot dog or the robot in a storage mode; a control module and a self-checking module are additionally arranged, the control module is integrated in a main control bin of the robot dog or the robot, and the self-checking module automatically executes a rapid self-checking process when the robot or the robot dog is started; the robot or the robot dog is started, the self-checking module executes the self-checking process, after self-checking is completed, if self-checking is normal, the cleaning system automatically enters a low-power-consumption standby state, whether cleaning is needed or not is judged, and if cleaning is needed, the cleaning liquid supply module and the high-pressure spraying module are controlled to conduct cleaning. According to the invention, a hidden arrangement and modular design mode is adopted, so that the core targets of traceless appearance, quick disassembly and assembly, low consumption, energy conservation and flexible upgrading are realized.

This invention discloses a device and method for measuring the spatial pose guidance accuracy of a robot vision system, relating to the field of robot guidance technology. The device includes a calibration flange mounted on a multi-degree-of-freedom motion platform. Three identical cylinders—a first cylinder, a second cylinder, and a third cylinder—are arranged on the end face of the calibration flange. The laser tracker and vision system can easily fit and identify the characteristic centers of the three cylinder end face circles on the calibration flange. Based on the identified end face circle features, the vision system guides the end effector of a robot tool to the corresponding position. The laser tracker measures the actual pose of the robot tool end effector after guidance, thus accurately measuring the spatial pose guidance accuracy of the robot vision system.

The application discloses an environment-friendly operation and maintenance robot vision system, which comprises an image acquisition module, an image processing module, a target detection module, a data conversion module and a result output module.The image acquisition module comprises a 3D camera arranged on a robot mechanical arm and is used for acquiring a planar image and a depth image of a target object.The image processing module is used for pre-processing the planar image acquired by the image acquisition module and conveying the planar image to the target detection module.The target detection module is configured with a rotating target detector trained based on a YOLOv8_OBB network model, is used for detecting the image conveyed by the image processing module, and outputs a target frame center point, a target frame width and height and a rotation angle.The data conversion module is used for obtaining a rotation and translation matrix of the target relative to the 3D camera.The result output module is used for converting the rotation and translation matrix into a relative motion matrix of the mechanical arm and outputting the relative motion matrix to a mechanical arm control unit of the robot.Based on the scheme of the application, an efficient and accurate water quality station robot vision system can be obtained.

A data collection apparatus for calibration of a robot vision system, according to the present invention, comprises a processor that carries out functions of collecting data for calibration of a robot, the functions comprising: acquiring images of a calibration sheet positioned at an arbitrary position at n reference viewpoints serving as a reference for an operating range of the robot; calculating calibration parameters on the basis of the images; applying the calibration parameters to generate and accumulate n mask images at the n reference viewpoints; calculating the position of a new viewpoint reachable by the robot other than the reference viewpoints; and determining a viewpoint direction for generating a new mask image that minimizes an area overlapping the accumulated mask images at the position of the new viewpoint.

This application relates to the fields of robot calibration and computer vision, and discloses a robust calibration method for a line laser robot vision system based on model reconstruction. The method includes: fixing a line laser sensor and initializing a hand-eye matrix; installing a freeform surface calibration object on the robot's end effector to acquire surface point clouds and end effector pose data in multiple poses; transforming the point cloud to the robot's end effector coordinate system based on the initial hand-eye matrix to obtain a reconstructed point cloud; searching for the nearest point from the reconstructed point cloud to the calibration object surface and calculating the point-to-surface distance residual; deriving the Jacobian matrix of the residual with respect to the current hand-eye matrix using a Lie algebra right-multiplication perturbation model; establishing an optimization function with residual minimization as the objective and introducing adaptive weights; solving for the hand-eye matrix error vector; and performing manifold updates on the hand-eye matrix until convergence. This application eliminates the dependence on standard parts, removes intermediate fitting errors, and suppresses measurement posture interference through a robust weighting mechanism, thereby improving calibration accuracy and robustness.

The application discloses a kind of robot vision system circular arc trajectory guide precision measuring device and method, it is related to robot guide technical field, robot vision system identifies the end face circle of the second cylinder of calibration flange, and generates point cloud based on the end face circle of the second cylinder, and the tool center point of vision system guided robot moves along the circumferential generatrix trajectory of the end face circle of the second cylinder, laser tracker measures actual motion trajectory after being guided by vision system in dynamic measurement mode, there is also the actual motion trajectory point position after being guided by vision system in laser tracking measurement system simultaneously based on the circumferential generatrix theoretical trajectory point position of the end face circle of the second cylinder, i.e., the guide precision of robot vision system spatial circular arc trajectory can be accurately measured and calculated.

This application discloses a robot head image recognition method and system, relating to the field of robot vision recognition technology. It aims to solve the problem in existing robot vision systems where image distortion caused by factors such as oil film on the lens surface affects the accuracy of image recognition in complex industrial environments. The method includes: acquiring the original image of the target to be recognized from the image sensor of the robot head; performing distortion pre-compensation processing on the original image to obtain an optimized image; performing feature extraction and recognition on the optimized image to obtain an image recognition result and a corresponding first confidence level; when the first confidence level is lower than a first preset threshold, determining the mechanical behavior confidence level based on the degree of matching between the mechanical state information and the expected mechanical state information corresponding to the image recognition result; and combining the first confidence level and the mechanical behavior confidence level, determining the final operation decision through a preset fusion decision rule.

The invention provides an automatic calibration method and system for a humanoid robot vision system and a medium, and the method comprises the steps: S1, obtaining a CSV configuration file which comprises a plurality of groups of predefined joint angle sequences meeting preset requirements; wherein each group of joint angle sequence corresponds to one calibration posture; s2, carrying out integrity verification on the obtained CSV configuration file; s3, controlling a target mechanical arm to move to a corresponding calibration posture according to a predefined joint angle sequence meeting a preset requirement in the verified CSV configuration file; s4, camera images, end effector poses and joint angle data are synchronously collected at each pose, and full-automatic batch collection of hand-eye calibration data is achieved; and S5, calculating a transformation matrix of the camera and the tail end of the mechanical arm through a hand-eye calibration algorithm on the basis of the camera image collected on each pose and the pose of the tail end executor.

The application discloses a caterpillar automatic pin pressing system and method, and belongs to the technical field of automatic assembly of engineering machinery. The system comprises a truss assembly, an aerial pin pressing machine, a robot, a vision system and a control system. The vision system is installed on the robot and is used for identifying the center of the pin guide end face of the caterpillar track link and generating position information; the control system controls the robot to grab the expansion pin and center the pin guide end face, and controls the aerial pin pressing machine to move to a pin pressing position so that the pin guide guide is tightly attached to the end face of the track link; during the pressing process, the robot and the aerial pin pressing machine dynamically cooperate and follow, and a displacement sensor monitors the pressing depth in real time. Through visual guidance and cooperative control, the application realizes the automation and precision of the expansion pin pressing, and improves the assembly efficiency, quality and safety.

The invention discloses a dynamic calibration method and system for a robot vision system based on data fusion, and relates to the technical field of robot vision calibration, and the method comprises the steps: obtaining a calibration plate image sequence and a joint torque signal of a robot, extracting the vibration characteristics of a candidate angular point, such as the angular point vibration amplitude, the angular point vibration frequency and the dominant resonance frequency; and judging whether the system is in a quasi-static state. In a quasi-static state, according to comparison of an average value, a maximum value and a standard deviation of angular point vibration amplitudes and a preset threshold value, dynamically determining an adjustment coefficient based on a comparison relationship between current and historical vibration characteristics in a self-adaptive manner, further calculating a self-adaptive data acquisition period, and acquiring additional image data; and then, multi-dimensional screening is carried out based on the spatial distribution characteristics of the constructed vibration feature vectors, an optimized data set is obtained, and finally, calibration is completed by adopting a robust estimation algorithm. According to the method, the calibration period precision and robustness of the robot vision system in the dynamic vibration environment are remarkably improved.

The application provides a robot zero-sample space perception and description generation method based on space-time feature alignment, and has the characteristics that the original image sequence obtained by the robot vision system is sequentially processed by the following steps: internal parameter blind estimation processing, feature anti-interference alignment processing, and detail perception enhancement processing based on a parameter efficient adapter, to generate a final perception result containing enhanced depth information and abnormal markers. Through the organic integration of the "internal parameter blind estimation module", "feature anti-interference alignment module" and "perception enhancement module", the application solves the problems of distance estimation deviation caused by lens change, feature jitter caused by dynamic vibration and insufficient perception of subtle edge features in the prior art, and realizes accurate, stable and high-precision space perception and zero-sample abnormal description of the robot in a complex environment.