203 results about "3d vision" patented technology

The invention discloses a stereoscopic vision optimization method and system for a naked-eye 3D large screen, and particularly relates to the technical field of naked-eye 3D vision optimizing.The method comprises the steps that environment illumination and audience positions are sensed in real time through multi-sensor fusion, and an environment light field model is established; glare crosstalk noise is predicted based on physical simulation, and self-adaptive suppression and compensation are carried out in combination with human eye visual sensitivity and image content features; a virtual camera is dynamically generated according to the real-time positions of the eyes of the audience, and a lightweight neural radiation field renderer is used for real-time re-rendering, so that motion parallax is realized; and finally, intelligently fusing the glare compensation layer and the perspective correction layer, coding and outputting to a screen. The system correspondingly comprises an environment perception module, a glare compensation module, a perspective rendering module and a fusion coding module. The naked-eye 3D large-screen display method effectively inhibits ambient light interference, improves the quality and immersion of a stereoscopic picture under different visual angles, and is suitable for naked-eye 3D large-screen display under outdoor and complex illumination environments.

The invention discloses an intelligent grabbing system anomaly detection and processing method and device based on 3D vision, and belongs to the technical field of industrial robot 3D vision guide grabbing. The method comprises the steps of collecting 3D point cloud data of stacked flexible plates; carrying out point cloud preprocessing and template matching, and determining a plate pose and a grabbing point; generating a cushion block detection area and a deformation detection area based on the grabbed point pose; counting the number of point clouds in the detection area, comparing the number with a threshold value, and judging whether cushion block residues or plate excessive deformation exists or not; if yes, triggering processing, and if not, outputting a grabbing instruction. The device comprises a truss manipulator, an end effector, a 3D visual sensor, a visual processing unit and a robot control unit. According to the method, the detection area is dynamically generated, point cloud threshold judgment is fused, self-adaptive detection and processing of excessive deformation of the cushion block and the plate are achieved, the robustness, safety and automation level of a grabbing system are remarkably improved, and the method is suitable for automatic unstacking operation of stacked flexible plates.

The invention discloses a robot disordered grabbing method and system based on 3D vision, and relates to the field of vision grabbing, and the method comprises the steps: configuring a 3D vision collection module to execute incoming material area continuous collection, executing definition of a robot base coordinate system, a camera coordinate system and an incoming material station coordinate system, and setting a tray in-place consistency constraint. Deterministic mapping of a clamp and a strategy is formed under multiple types of incoming materials, the grabbing success rate is improved, deformation and falling risks are reduced, collision safety is improved with minimum safety gap constraint and path cost selection in an unordered environment, and operation stability is maintained through action time accounting and beat conformity judgment on the beat side. And traceability is realized by a sorting accuracy target and a task log write-in mechanism, and finally, shutdown waiting and secondary faults are reduced through alarm and separate disposal rules in a static incoming material and structure separation risk scene.

The invention discloses a pig rib cutting path planning method based on 3D point cloud, and belongs to the technical field of intelligent cutting and robot automation. High-precision original point cloud of a rib area of a pig carcass is obtained in real time through a 3D visual sensor, and then a segmentation threshold value is dynamically adjusted by introducing a self-adaptive growth factor based on local point cloud density, so that the preprocessed point cloud is robustly and accurately segmented into rib and rib meat areas, and a centerline of a spine and a rib gap are synchronously positioned. And based on the segmentation result, a plurality of independent costal meat communication components are automatically identified and separated through voxelization clustering and graph theory connectivity analysis. For each component, the main extension direction of the component is determined by constructing an axis alignment bounding box, and a series of discrete path points closely attached to the actual surface of the point cloud are generated in the direction. Spatial fitting is carried out by utilizing a cubic polynomial curve to generate a smooth and continuous cutting path which is highly fit with an anatomical structure, and the cutting path is directly converted into an executable track of the robot.

The invention relates to the technical field of 3D visual inspection, in particular to a dynamic shielding area multi-view time sequence point cloud completion method and system in 3D visual inspection. The method comprises the following steps: collecting a multi-view time sequence image of a dynamic detection object for providing a data basis for feature point tracking; and carrying out Lucas-Kanade optical flow method tracking on the feature points, associating the feature points of different visual angles, describing a motion track of the feature points through an optical flow vector, carrying out cross-visual-angle and cross-time feature point matching, and establishing time sequence feature association to obtain time sequence feature points. According to the dynamic occlusion area multi-view time sequence point cloud completion method in 3D visual detection, 360-degree time sequence data of a dynamic detection object is acquired through multi-view time sequence image acquisition, and comprehensive basic data support is provided for subsequent feature tracking.

The invention relates to the technical field of intelligent welding, in particular to a steel structure fillet weld intelligent identification method and system based on 3D vision, and the method comprises the steps: obtaining a point cloud data set of a workpiece, and extracting point cloud data of a weld region from the point cloud data set; dividing the welding seam area into a plurality of welding seam sub-areas, wherein one welding seam sub-area corresponds to one independent welding seam; carrying out welding seam coarse skeleton extraction on each welding seam sub-region, and carrying out merging processing on the welding seam coarse skeletons of which the distance between end points is smaller than a threshold value; and on the basis of the combined welding seam coarse skeletons, welding seam fine skeleton point set extraction is conducted, and a welding seam path expression is obtained based on welding seam fine skeleton point set fitting. The method solves the problem that an existing algorithm needs an operator to repeatedly adjust a threshold parameter according to experience, resulting in lack of adaptive ability, and solves the problems that the calculated amount is large, the speed is low and the real-time requirement of an industrial site cannot be met when the workpiece point cloud data is processed.

The invention discloses a robot fine operation control system based on a diffusion model and 3D vision, particularly relates to the technical field of industrial robot fine operation control, and aims to solve the problem that when an existing industrial robot executes fine operations such as bottle cap screwing and connector plugging in and out in a complex three-dimensional operation scene, the operation efficiency is high. The problems that three-dimensional perception is unstable, the action resolution is fixed, space and contact constraints are difficult to meet, and the task success rate and safety are insufficient are solved. The method comprises the following steps: constructing a voxel feature field updated along with time under a self-body coordinate system, generating interaction complexity and a physical constraint field based on task coding, and linking adaptive resolution and action granularity scheduling, diffusion trajectory generation and closed-loop execution correction to uniformly map environmental geometry, contact conditions and task stages into tail end trajectory control, so as to realize real-time tracking control. Therefore, fine operations such as bottle cap screwing, connector inserting and pulling and flexible object folding can still be stably completed under complex shielding and contact constraint, and the effects of safety and task success rate are both considered.

The invention relates to a robot gear disc loading method based on 3D vision, and belongs to the technical field of industrial robot automation. The method comprises the steps that connection between a 3D camera and a robot is established, hand-eye calibration is completed, a template library of a gear type circular workpiece and a template library of a material disc type non-circular workpiece are established respectively, template matching pose output is carried out, and the robot gear disc loading precision is improved; outputting a high-precision pose matrix of the workpiece; and the grabbing points are optimally sorted according to the running direction of the robot, the placing points of the gears are sorted according to the preset sequence of the teaching points of the robot, and regular visual correction is executed to maintain the long-term precision of the system. The problems of low efficiency, error proneness, poor flexibility and the like of traditional manual tray loading are solved, high precision, high rhythm and full automation of multi-model gear tray loading operation are achieved, and the intelligent level and stability of a production line are remarkably improved.

The utility model relates to the field of gluing detection, and discloses an automatic gluing detection device based on 3D vision, the automatic gluing detection device comprises a base, a gluing machine and a detection table, the detection table is fixed between the inner sides of the base, the gluing machine is located on one side of the base, and the base is provided with a liftable moving frame for supporting a gluing head and a moving assembly for driving the moving frame to move horizontally. A liftable annular frame is arranged on the movable frame, and the gluing head can penetrate through the annular frame; the distribution radius and angle of the visual sensor can be dynamically adjusted through the hinge rods and the folding and unfolding assemblies which are circumferentially distributed on the annular frame, when a deep groove and a corner area are detected, the hinge rods retract to enable the sensor to be close to the surface of a workpiece, then the local resolution can be improved, and when a large-area curved surface is detected, the hinge rods are unfolded to expand the detection range; the three-dimensional curved surface of the workpiece surface is fully covered, the problem of defect missing detection of the corner area of the deep groove can be effectively solved, and the detection efficiency can be improved.

The application relates to the technical field of image processing, in particular to an AGV positioning method and system based on 3D vision, which comprises the following steps: acquiring images collected by AGV and constructing a source point cloud, performing iterative pose optimization, calculating the expected depth of a pixel point based on a current pose and a static map, calculating the depth residual and the uncertainty of the depth residual for each pixel point; fusing the two to determine the obstacle probability of the pixel point, performing 2D convolution on the obstacle probability to determine the spatial support factor; fusing the depth residual and the uncertainty and the spatial support factor through a fusion penalty model to determine the anti-occlusion confidence of the pixel point, taking the anti-occlusion confidence as a dynamic weight, performing a weighted ICP algorithm to solve a new pose, and outputting the positioning result of the AGV until convergence. The method suppresses the interference of dynamic obstacles and their edge artifacts on positioning, and improves the positioning accuracy of the AGV.

This invention proposes a method and system for detecting missing weld points on vehicle bodies based on 3D vision and global matching, belonging to the field of image recognition technology. The method includes: determining the transformation relationship between the camera coordinate system and the vehicle body coordinate system through calibration; at each observation position, a 3D camera acquires an image I containing a frame of 2D texture and the corresponding 3D data; inputting the acquired image I into a pre-trained deep learning object detection model, outputting the bounding box information (u, v, w, h) for each weld point; for each detected weld point, calculating the set S of the weld point's 3D coordinates in the vehicle body coordinate system. det For the theoretical solder joint library S cad Each theoretical solder joint P in cad,i In set S det The invention employs a "detect first, match later" strategy, which does not rely on the precise projection of the theoretical position, thus reducing the requirements for the positioning accuracy of the production line tooling fixtures and conveying system.

The invention relates to the technical field of image processing, in particular to an AGV positioning method and system based on 3D vision, and the method comprises the steps: obtaining an image collected by an AGV, constructing a source point cloud, executing the iterative pose optimization, calculating the expected depth of pixel points based on the current pose and a static map, calculating a depth residual error per pixel point, and the uncertainty of the depth residual error; fusing the two to determine the obstacle probability of the pixel point, and performing 2D convolution on the obstacle probability to determine a space support factor; and the depth residual error, the uncertainty and the space support factor are fused through a fusion penalty model, the anti-shielding confidence coefficient of the pixel point is determined, the anti-shielding confidence coefficient is used as a dynamic weight, a weighted ICP algorithm is executed to solve a new pose, and the AGV positioning result is output after convergence. According to the method, the interference of dynamic obstacles and edge artifacts thereof on positioning is suppressed, and the positioning accuracy of the AGV is improved.

The invention discloses a self-adaptive hybrid control grinding method and system for a steel rail welding seam. The method comprises the steps that a welding seam three-dimensional model is obtained through 3D vision, and material removal amount distribution is calculated; intelligent layered path planning is conducted based on the distribution, specifically, process threshold layering is set, section sampling is conducted to generate tool points, collision-free optimization is conducted, an initial polishing path is generated, and expected contact force is matched; in the polishing process, the contact force is collected in real time through a six-dimensional force sensor; feedforward control is carried out on the material removal amount based on visual prediction, feedback compensation is carried out through a fuzzy PID controller on the basis of force deviation fed back by a force sensor, motion parameters of the robot are dynamically adjusted, and double-layer self-adaptive control of visual intelligent planning and force control accurate execution is achieved. The system comprises corresponding modules. The problems that an existing method is poor in adaptability to geometric differences of the welding seams and lacks macroscopic planning are solved, and high-precision, high-efficiency and high-consistency automatic grinding of the steel rail welding seams is achieved.

The invention relates to the technical field of high-end equipment manufacturing, and discloses an intelligent production line for new energy automobile support part manufacturing and an operation method and application of the intelligent production line. The production line comprises a storage and dispatching unit, an AGV logistics and transfer unit, a 3D visual recognition and positioning unit, a self-adaptive intelligent clamping unit, a multi-axis numerical control machining center unit and an online detection and quality feedback unit. The production line carries out global optimization production scheduling through a central scheduling model, rapid remodeling and accurate clamping are achieved through 3D vision and a flexible clamp, parameters are adjusted in real time through a self-adaptive model according to force and vibration signals in the machining process, and a quality closed loop is formed through online detection. The problems of frequent die change, low efficiency and poor precision consistency in a multi-variety small-batch production mode are effectively solved, high-degree flexible production is realized, the production efficiency and the product quality stability are remarkably improved, and the dependence on manpower is reduced.

The invention provides sealing ring height detection equipment based on 3D vision. The sealing ring height detection equipment comprises a case, a turnover mechanism, two transportation mechanisms and two detection mechanisms. The overturning mechanism comprises an overturning driver, a rotating shaft and a plurality of clamping assemblies, the overturning driver is installed in the machine box, the rotating shaft is installed at the output end of the overturning driver and rotates around the first direction under driving of the overturning driver, and the clamping assemblies are all installed on the rotating shaft. The two conveying mechanisms are both installed in the machine box and arranged on the two sides of the overturning mechanism in the second direction, one conveying mechanism is used for inputting the workpieces into the clamping assembly, and the other conveying mechanism is used for outputting the workpieces from the clamping assembly. The two detection mechanisms are installed in the machine box and correspond to the two conveying mechanisms in a one-to-one mode. The sealing ring height detection equipment based on 3D vision is high in automation degree, high in detection efficiency and small in detection structure error, and the manual labor intensity can be reduced.

This invention discloses an automated tire warehousing system and method for truck cranes, belonging to the field of intelligent manufacturing and logistics automation technology. The system integrates a WMS (Wastewater Management System), MES (Manufacturing Execution System), a central control scheduling system, AGVs (Automated Guided Vehicles), a gantry robot, and a dual-mode vision system. The method is as follows: the MES issues orders, and the central control system breaks down tasks; the WMS schedules AGVs to return empty pallets and deliver tires to the production line; the gantry robot uses 2D vision to scan the tire's QR code for identity verification; if the verification is correct, the tire enters assembly; otherwise, it is returned and reassembled; after assembly, the central control system triggers the warehousing task, the WMS dynamically allocates the optimal storage location, the AGV transports the tire to the temporary storage area, and the gantry robot, guided by 3D vision, accurately grasps and places the tire into the storage location; the WMS updates the inventory. This invention achieves full-process automation and information closure of tires from production line to warehousing, significantly improving efficiency, accuracy, and warehouse utilization, while reducing costs and safety risks.

This invention discloses a method and system for feature extraction of multi-layer stacked cardboard boxes based on 3D vision. Addressing the problem that existing technologies cannot accurately quantify surface anomalies and multi-dimensional stacking features of cardboard boxes in complex stacking scenarios, this invention acquires the original point cloud and RGB images of the stacking scene; after preprocessing, a height distribution histogram is constructed and clustered to obtain initial point cloud clusters; warped anomalies are eliminated through curvature analysis and normal vector consistency to obtain accurate upper surface point clouds; principal component analysis is used to obtain the length, width, and normal vector direction of the cardboard box; basic geometric features, damage features, occlusion relationships, overhang / overlap features, tilt features, and multi-layer interlacing features are extracted by combining RGB images; finally, the feature vector of each cardboard box is output in a structured manner. This invention achieves refined point cloud processing, effectively eliminates warped points, systematically quantifies complex stacking features, and integrates multimodal data, significantly improving the ability to identify and extract features from cardboard boxes in complex logistics scenarios.

The present application relates to the technical field of hub polishing robots, and particularly relates to a hub polishing robot trajectory planning method based on 3D vision curve technology; by comparing an actual 3D curved surface graph with a preset 3D curved surface graph, a to-be-polished region model graph of each to-be-polished region in a target hub is generated; according to the to-be-polished region model graph of each to-be-polished region, optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are obtained by pairing in a database; according to the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub, optimal polishing trajectories of a polishing robot are planned; and the optimal polishing trajectories of the polishing robot and the optimal polishing parameters for polishing processing of each to-be-polished region in the target hub are sent to a control terminal. More accurate, self-adaptive, efficient and consistent polishing effects can be achieved, and production efficiency and product quality are improved.

The invention relates to the technical field of intelligent welding repair of molds, in particular to a method for intelligently welding and repairing a mold through a robot. The method comprises the following steps: performing carbon gouging on a worn part of a mold cavity to form a pit to be repaired; the robot scans and obtains three-dimensional point cloud data of the pit through a 3D vision camera; the system extracts contour and depth features and completes positioning; the total welding layer number is automatically calculated according to the depth features and the process parameters, and the welding track and the welding channel number covering each layer are planned for each layer based on the contour features of the layer; carrying out physical calibration on a welding starting point by adopting contact type locating; the robot carries out automatic welding filling layer by layer and path by path according to the calibration starting point and the planned path; and through multi-station arrangement, robot automatic welding and manual carbon gouging pretreatment of another station are in parallel cooperation, and circulating production is formed. According to the method, teaching-free and self-adaptive repair of the three-dimensional pit is realized, and the repair quality, efficiency and safety are remarkably improved.

The invention discloses stacking equipment for stone-plastic floor production, and belongs to the technical field of stone-plastic floor production. Stacking equipment for stone-plastic floor production comprises a conveying belt, a carrying robot, a stacking frame and an automatic discharging box, and an RFI3D reader-writer and a 3D visual camera are installed on the surface of a sensor support; a vacuum chuck is mounted at the upper end of the transfer robot; stacking shelves are stacked in the automatic discharging box, and an electric push rod used for pushing the stacking shelves onto the stacking shelves is installed at the bottom of the automatic discharging box. The problems that an existing stone-plastic floor stacking device is single in structure and cannot be adjusted according to the position and the number of specific stone-plastic floors, and continuous work is affected are solved, the position angle of the stone-plastic floors under a vacuum suction cup is detected through a 3D vision camera on a sensor support, a carrying robot is adjusted through an industrial PLC, and the working efficiency is improved. The discharging and stacking positions of the transfer robot are automatically adjusted, the working requirement is met, and the transfer robot adapts to the working environment.

The invention discloses a robot intelligent welding unit and method based on 3D visual guidance, and the unit comprises an industrial robot arm, a welding gun, welding equipment, at least one 3D visual sensor and a visual processing controller. A three-dimensional reconstruction module, a self-adaptive path planning module, a real-time feedback control module and a safety monitoring module are integrated in the visual processing controller; a workpiece is scanned through the 3D vision sensor, a three-dimensional model is reconstructed, a welding seam is automatically recognized through the self-adaptive path planning module, an optimized welding path with embedded technological parameters is generated, and in the welding process, the real-time feedback control module dynamically corrects the robot track and adjusts the welding parameters based on a prediction-correction strategy, so that the welding quality is improved. And meanwhile, the safety monitoring module utilizes real-time point cloud to construct a virtual fence for collision protection, so that the whole-process intelligence from pre-welding sensing and autonomous planning to real-time correction and safety monitoring in welding is realized, and the welding precision, the process adaptability and the operation safety are greatly improved.

The application discloses an automatic quantitative glue coating and plugging system based on 3D vision, which comprises an industrial computer, a movable platform, a mechanical arm, a 3D camera, a plug stock bin assembly and a terminal device; the mechanical arm is provided with the 3D camera and the terminal device at the tail end; the terminal device comprises a pneumatic gripper assembly, a glue coating assembly and a glue scraping plate; when coating, the industrial computer controls the mechanical arm to drive the 3D camera to move to the top of a hole to be coated to obtain point cloud data of the hole to be coated, and processes the point cloud to obtain the center coordinates, the radius and the depth of the hole to be coated; the industrial computer calculates the glue coating amount according to the radius and the depth of the hole and the glue coating thickness; the plug stock bin assembly pops out a plug matched with the radius of the hole; after the industrial computer controls the pneumatic gripper to take out the popped plug, the industrial computer controls the glue coating assembly to move to the center coordinates to coat glue according to the calculated glue coating amount, and after coating, the industrial computer controls the pneumatic gripper to put the taken plug into the hole.

The present application relates to the technical field of industrial vision detection and intelligent manufacturing, and in particular provides a papermaking wood grading evaluation method based on 3D vision and high-resolution imaging fusion. The method comprises raw material feeding and pre-screening, and collecting front and back multi-modal information to realize heterogeneous data archiving, establish an initial digital archive of the target physical form; camera alignment and foreground segmentation are performed, and material categories and associated metadata are identified to obtain offline grading evaluation. Through hardware synchronization and algorithm cooperation, the method realizes automatic collection of front and back of wood, calculation of three-dimensional geometric parameters, material classification and grading evaluation, significantly improving detection accuracy, reviewability and automation level.

The invention discloses a three-dimensional space material grabbing method and device and a vehicle assembling system, and relates to the technical field of process control. Comprising the steps of determining two-dimensional coordinates of a material according to an image of the material; the sensing arm extends to the material from the first position until the sensing arm makes contact with the material so as to obtain a second position when the sensing arm makes contact with the material; according to the first position and the second position, coordinates of the material in a third dimension perpendicular to a two-dimensional plane corresponding to the two-dimensional coordinates are determined; and the materials are grabbed according to the two-dimensional coordinates of the materials and the coordinates of the materials in the third dimension. According to the technical scheme, compared with an existing 3D visual recognition technical scheme, the same grabbing function can be achieved, and the equipment cost is greatly reduced. Through measurement and calculation, the 2D vision cost of photographing to obtain images is reduced by one order of magnitude compared with the 3D vision cost, so that the method has a wide application prospect.

The application discloses a kind of multi-station coordinated stator wire harness hot sleeve assembly method and system, it is related to motor component assembly technical field;Construction station twin model, AGV distributes material with RFID label according to tempo, PLC dynamic replenishment;After wire stripping and tin, 3D vision guides mechanical arm and inserts wire according to slot, force control adjusts insertion angle;Double-shaft platform adjusts stator and heating coil coaxial alignment, infrared sensor aims sleeve;PLC controls heating power, time, rotates stator to ensure uniform heating, mechanical arm advances sleeve and pressure monitoring;Air cooling-water cooling-natural cooling, temperature feedback adjusts parameter;Insulation, appearance, conduction, tension detection, data binding stator ID upload traceability.The application improves efficiency through digital twin, optimizes hot sleeve parameters to ensure fit quality, graded cooling enhances stability, full-dimension detection and traceability improves controllability, intelligent early warning reduces maintenance cost, adapts to batch production demand.

The application provides a mobile robot control method and device for server carrying, the method comprising: realizing accurate positioning and mapping through a multi-sensor fusion SLAM navigation technology, accurately identifying a target cabinet through a 3D vision system and obtaining installation position information, calculating deviation parameters of a current pose of the server and a target installation position through a feature matching algorithm, and coordinating pose adjustment of a cargo platform and a lifter body based on deviation compensation. Through an integrated multi-dimensional pose compensation control mechanism, the application realizes full-process automatic control of the mobile robot from navigation positioning to accurate alignment, and effectively improves the positioning accuracy and success rate of automatic server installation.

The invention discloses a system and a method for realizing automatic splicing of small pieces of a movable arm of an excavator, and belongs to the technical field of engineering machinery, and the method comprises the following steps: identifying the types and the quantity of small piece materials by utilizing a 3D visual system to obtain a small piece identification result; the carrying robot is controlled to grab the small parts, the small parts are accurately positioned through the small part secondary positioning mechanism, and the positioned small parts are obtained; position deviation correction is carried out on the movable arm body transported by the AGV through the movable arm body secondary positioning mechanism, the actual space coordinates of the movable arm body in the work station are obtained through the laser locating system, and the corrected workpiece coordinates are obtained; and the carrying robot is controlled to place the positioned small part at the designated position of the movable arm body, and the welding robot and the welding positioner are controlled to cooperatively complete spot welding and full welding of the small part. According to the invention, automatic identification, grabbing, locating and mistake proofing of small movable arm piece materials are realized, full-automatic point splicing of the small movable arm pieces is completed, the manual participation degree is reduced, and the offline consistency rate of products is improved.

The application discloses a kind of plane weld identification method and system based on 3D vision point cloud, it is related to welding seam identification technical field, including steps: to initial workpiece three-dimensional point cloud is preprocessed to obtain denoising workpiece three-dimensional point cloud;Two welding point cloud clusters are obtained by carrying out point cloud segmentation to denoising three-dimensional point cloud;Two welding point cloud clusters are respectively carried out point cloud edge point detection to obtain cluster edge point cloud set;Weld detection and weld line fitting are carried out based on two cluster edge point cloud sets, and fitted weld center line and fitted weld start and end point are obtained.The method provided by the application, when two welding point cloud clusters are obtained by carrying out point cloud segmentation to denoising three-dimensional point cloud, point cloud segmentation is carried out based on centroid direction vector, unlike the method that traditional needs to carry out clustering to a large number of point clouds, greatly improves the segmentation efficiency;Bias mechanism is introduced, in the case where there is deviation in the initial positioning of segmentation center, it can be automatically adjusted to the best segmentation position, guarantee the accuracy and robustness of segmentation.

The invention discloses a disordered blade grabbing system and method based on mechanical arm visual guidance and rotating target detection. The system comprises a mechanical arm, a tool clamp, a 3D visual module, an electric clamping jaw, a mechanical arm controller, a control component and the like. The method comprises the following steps: acquiring an RGB picture and a depth picture of the top of a leaf box; using a rotating target detection method to detect blade capture features on the RGB picture to obtain two-dimensional coordinate information of a target point, and calculating three-dimensional coordinates of the target point of the capture features in a camera coordinate system by combining depth information in the depth picture and camera internal reference; coordinate information of a mechanical arm is calculated when the action point of the clamping jaw coincides with the blade grabbing feature target point; and finally, according to the deflection angle of the blade grabbing characteristics obtained in the rotating target detection process, the optimal grabbing pose of the mechanical arm is obtained, and the mechanical arm and the electric clamping jaw are controlled to operate cooperatively. According to the blade feeding device, the problem of dependence on manpower in the blade feeding process is solved, the production efficiency is improved, and the production cost is reduced.

The invention discloses a battery box grabbing method, a battery box storing method and a battery replacing robot. The battery box grabbing method comprises the following steps that a 3D visual camera outputs pose information of a battery box to a controller; the extending angle of the mechanical arm is adjusted till the mechanical arm is perpendicular to the side face where the recognition block of the battery box is located; the grabbing piece descends and moves towards the battery box from the upper part of the battery box; a first distance and a second distance to the battery box are detected, and if the deviation between the first distance and the second distance is within a preset range, it is determined that the posture of the grabbing part is normal; and the controller controls the grabbing piece to grab the battery box. According to the battery box grabbing method and the battery replacing robot, the posture of the grabbing piece relative to the battery box can be confirmed by combining the 3D visual information and feedback of the sensor in the grabbing process, and therefore the grabbing reliability can be improved on the basis of low economic cost.