41 results about "Robot workspace" patented technology

The invention relates to a double-robot automatic calibration method based on self-adaptive point location planning in a restricted space, and belongs to the technical field of double-robot calibration, and the method comprises the steps: building a three-dimensional simulation model of a double-robot operation space on an Rviz platform based on an ROS architecture; a collision detection module is used for randomly generating a calibration point composed of collision-free double-robot joint angle vectors; when the robot is driven to move, data of the motion capture camera and positive kinematics Cartesian coordinates of the double robots are synchronously collected, a calibration result is obtained through optimization of a least square method by adopting a hand-eye calibration algorithm, and the effectiveness of the calibration result is judged through the average value of data calibration precision until calibration of the double robots is completed. Simulation modeling, collision detection and a hand-eye calibration technology are fused, a complex calibration problem is converted into a virtual environment parameter optimization process through a self-adaptive sampling strategy and a data synchronous acquisition mechanism, calibration robustness and precision are remarkably improved, and guarantee is provided for high-precision cooperative control of double robots.

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.

Provided in the present invention are a virtual-robot and physical-robot collaborative safety control method and apparatus based on mixed reality and a digital twin, which method and apparatus specifically relate to the technical field of smart manufacturing and assembly involving virtual-robot and physical-robot collaboration. In the solution, the method comprises: acquiring first posture information of a physical robot, and acquiring second posture information of a virtual robot; on the basis of the first posture information and the second posture information, performing pose registration on the physical robot and the virtual robot, so as to obtain a registration result; on the basis of the registration result and a preset virtual-robot and physical-robot workspace in combination with deep reinforcement learning, performing path planning to determine a virtual-robot and physical-robot collaborative motion path; and on the basis of the virtual-robot and physical-robot collaborative motion path, executing a virtual-robot and physical-robot collaborative safety control action. The solution uses a mixed-reality device to extract virtual-robot and physical-robot posture information, implements virtual-robot and physical-robot pose registration by means of a virtual-real space mapping method, and designs a bidirectional virtual-robot and physical-robot collaborative safety interaction policy based on deep reinforcement learning, thereby effectively improving the safety and effectiveness of a virtual-robot and physical-robot interaction operation.

The invention discloses an automatic gearbox spindle assembly part sorting and dispatching logistics system and method. A standby material area, a sorting material area, a robot working area and a sorting station conveying line are arranged in parallel. The sorting station conveying line is sequentially provided with an empty frame station, a working station and a discharging station in the conveying direction. The working positions are provided with a code scanning position, a working position 1, a cache position 1, a cache position 2 and a working position 2 in the conveying direction; the standby material area and the sorting material area are sequentially a first-shaft gear placing area, a first-shaft differential gear placing area, a second-shaft first-gear gear placing area, a second-shaft second-gear gear placing area, a second-shaft third-gear gear placing area, a second-shaft reverse-gear gear placing area and a second-shaft placing area in the conveying direction of the sorting station conveying line. Repeated ex-warehouse and in-warehouse operation is avoided, switching is smoother, production switching time is greatly shortened, work order switching time is saved by about 30%, data transmission interfaces are reduced, data chaos caused by network fluctuation or interface jamming is prevented, and data are more accurate.

The invention discloses a double-station robot control method and terminal equipment based on clean panel sorting, and the method comprises the steps: obtaining three-dimensional point clouds and two-dimensional images of different clean panels and protection plate sleeves, and carrying out the analysis to obtain material features; by acquiring the three-dimensional point cloud and the two-dimensional image, the spatial form and surface information of the material can be comprehensively and accurately captured, and a rich and accurate data basis is provided for subsequent feature analysis. In practical application, different panel types and panel sleeve materials are obviously different in three-dimensional structure and two-dimensional vision, and feature analysis results become important basis for identification and sorting. According to the double-station robot control method based on clean panel sorting and the terminal equipment, the space-time coordination model is adopted to optimize the double-station coordination path according to the robot working space and obtain the optimal coordination path, the robot working space can be fully utilized, the double-station action conflict is reduced, and the working efficiency is improved. And the double-station collaborative operation efficiency and fluency of the robot are improved.

The utility model provides a gantry machining center capable of automatically discharging, which comprises a base, a gantry truss robot mounted on the base, a power tool apron mounted at the free end of the gantry truss robot, a machine tool table arranged below the gantry truss robot and positioned in a working area of the gantry truss robot, a plurality of mounting seats are mounted in the supporting frame, at least two supporting seats are arranged below each mounting seat, the supporting seats are arranged around a workpiece, a first telescopic mechanism is mounted at the bottom of each supporting seat, a clamping seat is mounted at the free end of each first telescopic mechanism, and lifting frames are arranged on the two sides of the supporting frame correspondingly; the bottoms of the lifting frames are slidably arranged on the supporting guide rails, and the lifting frames are driven by the linear driving module to run on the supporting guide rails. When in use, workpieces can be automatically carried during working, manual carrying is not needed, and time and labor are saved when the workpieces with large quality are machined.

The application discloses a pixel-based working area planning method, a chip and a robot. The working area planning method comprises the following steps: counting the number of obstacle pixels in each row and each column in a preconfigured map area in a map constructed by the robot; and expanding the number of obstacle pixels in each row and the number of obstacle pixels in each column to obtain a working area of the robot, which is used as a first working area of the robot. The method can simplify the planning of a working path of an indoor working area, and can also traverse to a sufficient number of open areas at a distance as far as possible, so that the robot moves to more effective working areas in the first working area.

A robotic system, method, and apparatus for controlling robot operation are disclosed. The robotic system includes: (i) a robot configured to move one or more items within a workspace; (ii) sensors configured to collect sensor data about the workspace; and (iii) one or more processors. The one or more processors are configured to: (a) determine to reset operation of the robot; (b) determine, based at least in part on the sensor data, that a human worker has exited a safeguarded space within the workspace; and (c) resume operation of the robot in response to determining that the human worker has exited the safeguarded space.

The invention discloses a camera precision calibration method and system based on 3D structured light. The method comprises the steps that a calibration reference object is preset in a robot working space; controlling the 3D structured light camera to carry out multi-view shooting on the calibration reference object according to a preset track or position point, and collecting an image and point cloud data of the calibration reference object; processing the acquired image and the point cloud data, and extracting feature information on the calibration reference object; calculating internal parameters and external parameters of the 3D structured light camera according to the feature information, and performing error compensation on a measurement result of the 3D structured light camera according to the internal parameters and the external parameters; and automatically verifying the calibration result, and judging whether the calibration is qualified or not. According to the method, the efficiency is improved, the precision loss caused by human errors is avoided, the calibration precision is improved, a reliable data basis is provided for subsequent welding tasks by introducing a precision verification link, and the consistency of the welding quality is improved.

The application belongs to the field of path planning, and particularly relates to a robot formation planning method based on a five-dimensional configuration space. The method comprises the following steps: workspace and configuration space definition: the workspace is a two-dimensional plane for robot movement, and the configuration space is a five-dimensional space containing formation centroid, direction and scaling factor; sampling method: random sampling in the configuration space, and ensuring that the sampling points do not cause collision between robots; distance function: defining the distance between two points in the configuration space, and eliminating the dependence on the number of robots; search: searching in the configuration space using an algorithm, and constructing a collision-free path tree; path generation: converting the path in the configuration space into the path in the robot workspace, and smoothing using a spline curve. The robot formation can pass through a narrow channel, and split and merge when encountering a large obstacle. The application provides a high-efficiency, flexible and collision-free global path planning method for a multi-robot system.

The utility model provides a kind of composite handling operation robot, including fixed frame, is installed in the power output end of truss robot;The region of setting truss robot is also provided with stacking area, for stacking material;The lower end of fixed frame is provided with gripper, and the material of stacking area is grabbed by its opening and closing;The lower end of fixed frame is also provided with material frame grabbing pawl for grabbing vinasse frame and driving vinasse frame rotation, and lifting assembly for driving material frame grabbing pawl;The highest point of material frame grabbing pawl is higher than gripper, and the lowest point is lower than gripper.Through setting stacking area in the working area of truss robot, so that when transport vehicle does not arrive in time, vinasse can still be transferred from conveying line and temporarily stored in stacking area, avoid vinasse frame to be accumulated and jammed in the end of conveyor, so as to ensure that vinasse production line can be continuously and stably operated, eliminate the production bottleneck caused by vehicle delay.

The application relates to a kind of parallel robot work space efficient solving method based on seed space segmentation and principal component analysis, the method uses Monte Carlo and stratification method as the basis of solving algorithm, through the fusion of both, in addition to the seed space segmentation for boundary space screening and the principal component analysis algorithm for the stratification direction of boundary seed space, the design of overall solving algorithm is realized.The method can segment the larger work space of parallel robot, and the rough boundary of work space is quickly and independently screened out by the ratio of reversible point and non-reversible point in the segmented space, the range of overall solving space is reduced, and the optimal stratification direction of boundary seed space is selected by the principal component analysis method, the number of stratification layers in the direction with large gradient change is increased, the feature loss is reduced, and the solving accuracy of overall work space boundary is improved.

The application provides a robot anti-collision method, device and robot control system, and the method comprises the steps of establishing a workpiece coordinate system, constructing a robot workspace under the workpiece coordinate system, obtaining a first pose of a tool end of the robot moving to a target point of the robot workspace, calculating a second pose according to the first pose and a first coordinate system transformation matrix, the second pose being a pose of the tool end in a robot base coordinate system, and the first coordinate system transformation matrix being a matrix for coordinate conversion between the workpiece coordinate system and the robot base coordinate system, calculating an enclosing box of each joint connecting rod and the tool of the robot in the robot base coordinate system according to the second pose, obtaining a plurality of base coordinate enclosing boxes, the enclosing box being a smallest cuboid wrapping the joint part, and the joint part comprising the joint connecting rod and the tool, and determining whether collision occurs between the joint parts corresponding to the plurality of base coordinate enclosing boxes by using the separate axis theorem, so that the problem of high cost of anti-collision through three-dimensional vision technology is solved.

The application provides a grinding device suitable for large special-shaped castings, which comprises a grinding chamber, the space in the grinding chamber is divided into an A working area and a B working area, the A working area and the B working area are independently arranged, and a workbench is arranged in each of the A working area and the B working area; a first grinding robot and a second grinding robot, and the first grinding robot and the second grinding robot are respectively installed at the seventh axis of the two robots. Through the double-robot layout of the first grinding robot and the second grinding robot and the double-working-area layout of the A working area and the B working area, the application realizes the parallel processing of the continuous polishing of the robot and the manual feeding and discharging, eliminates the waiting time, and improves the robot utilization rate.

The invention provides an intelligent robot action planning method based on reinforcement learning, and the method comprises the steps: enabling a strategy network to obtain scene features and historical actions according to the current environment observation and the last moment action; inputting the data and tiny time migration into a neural radiation field-physical dynamics coupled world model, and generating output containing physical state increment after model processing; the strategy network executes physical consistency state query on robot working space sampling points, physical state increments generated by a world model and static scene features calculated based on scene features and sampling point positions are added, predicted features are obtained and aggregated, and a physical consistency prediction state is generated; and a current action is generated accordingly. According to the method, physical consistency prediction of a future instant scene state generated by the model is taken as decision input, and physical prediction consistency is taken as a reward item cooperative training strategy, so that the strategy has excellent zero-crossing sample generalization ability in an unknown real environment, and semantic errors of simulation and reality are bridged.

The invention provides a berry lossless sorting method and system based on heterogeneous visual information space-time registration, and belongs to the technical field of agricultural product automatic lossless detection and sorting. According to the method, when berries move along with a conveying belt, image sequences are collected through a linear array hyperspectral camera and a visible light camera, and internal quality features and center coordinates are extracted. The fixed linear edge of the conveyor belt is used as a unique space reference datum, the projection distances from the feature information and the coordinates to the datum are calculated, matching is conducted through the sequence of the projection distances, and the corresponding relation between the quality and the position is established. And then, the escape distance of the berries relative to the mechanical arm movement boundary (namely the downstream boundary of the working area of the parallel robot) in the conveying direction is calculated, a grabbing priority queue is dynamically generated from near to far according to the escape distance, and the parallel robot is controlled to complete sorting according to the quality. According to the method, the problem of dislocation of asynchronous collection of heterogeneous visual information is solved through a software algorithm, and the sorting omission rate is remarkably reduced through dynamic priority scheduling.

The utility model discloses small lithium battery sorting equipment in the field of battery sorting, and aims to solve the problem of wrong sorting of batteries in the prior art, the small lithium battery sorting equipment comprises a belt transmission device, a robot and a sorting data MES (Manufacturing Execution System), the belt transmission device comprises a code scanning area, a jet printing area and a robot working area, a sensor is arranged in the robot working area, and the sorting data MES is connected with the robot. The sensor is used for sensing the number of batteries located in the working area of the robot, and starting and stopping of the belt conveying device are controlled according to the number of the batteries. The battery sorting device is simple in structure, workers do not need to participate in sorting, the sorting data MES system sprays generated grade codes on batteries, then the batteries are placed in the extraction trays of the corresponding grade codes through the robot, and the belt conveying device is provided with the sensor in the robot working area to sense the number of the batteries entering the robot working area. And disorder caused by accumulation of the batteries in a robot working area is avoided, and the sorting accuracy is ensured.

The application discloses an intelligent robot control system based on Internet of Things, and relates to the technical field of robot control.The system comprises a control center and a robot control terminal, wherein the control center is connected with the robot control terminal through wireless communication; the control center comprises a space construction module, which constructs a digital twin map of a robot workspace; a region marking module, which is used for marking a walking road of the robot on the digital twin map, dividing the walking road into a plurality of walking sub-sections, and evaluating environmental complexity indexes of each walking sub-section; and a unified planning module, which is used for planning a walking control path of each intelligent robot.The application has the advantages that the dynamic obstacle intrusion frequency and other environmental complexity indexes of each sub-section are accurately quantified, and the differences in real-time obstacle avoidance computing power of the robots are fused, so that the safe, efficient and collaborative operation of the intelligent robot group is achieved.

The application discloses a kind of robot autonomous exploration methods based on deep reinforcement learning, including the data information of surrounding environment that robot real-time obtains;Initial environment map of robot workspace is constructed;Incremental construction map and detect the boundary and information gain of map;Obtain the final environment topology map;Robot autonomous detection learning model is constructed;Robot uses autonomous exploration learning model to explore surrounding environment and calculates to obtain the execution strategy of next step;Repeat the above steps to complete robot autonomous exploration based on deep reinforcement learning.The application breaks the dimension curse of action space and observation space, while not losing the structural properties of workspace;Model-free reinforcement learning technology is used to learn exploration strategy in the process of trial and error interaction with physical or simulation system, without human intervention;Moreover, the method of the application realizes the autonomous exploration of robot, with high reliability, good accuracy and high efficiency.

The invention discloses a working space optimization method for a double-arm robot, and belongs to the field of robots. The method comprises the steps that D-H coordinate system parameters of the two mechanical arms are obtained; according to the D-H coordinate system parameters of the mechanical arm, the left-right limit carrying distance and the front-back limit carrying distance of the double-arm robot are calculated; and according to the left-right limit carrying distance and the front-back limit carrying distance of the double-arm robot and the actual carrying work space requirement, the base parameters of the double-arm robot are determined. The device comprises a parameter acquisition module, a calculation module and an optimization module. According to the technical scheme, the optimal mechanical arm parameters are obtained through calculation, maximization of the carrying working space of the double-arm robot is achieved, and the working efficiency and flexibility of the mechanical arm of the double-arm robot are improved.

The invention relates to a method and device for determining the working space of a spraying robot, equipment, a storage medium and a program product. The method comprises the steps that according to orientation information of a to-be-sprayed surface of a to-be-sprayed body, related information of a spraying robot and size information of the to-be-sprayed body, a first reachable working space, meeting the spraying main normal vector direction, of the spraying robot is determined; according to the first reachable working space and the orientation information of the surface to be sprayed, a second reachable working space, meeting the normal vector deviation, of the spraying robot is determined; and according to the second reachable working space, the spraying speed of the spraying robot, the size information of the guide rail where the spraying robot is located and the running speed of the spraying robot on the guide rail, a target working space when the spraying robot is linked with the guide rail is determined. By adopting the method, the spraying efficiency of the spraying robot can be improved.

A method, device and storage medium for visual servo control of a neurosurgical robot The present invention relates to the field of medical equipment technology, and discloses a method, device and storage medium for visual servo control of a neurosurgical robot. The method comprises: modeling obstacles in the clinical surgical environment; constructing a virtual artificial potential field in the robot workspace in combination with the target puncture posture; establishing a six-dimensional force spinor acting on the end of the robot based on a two-point force constraint; modeling the visual servo tracking process guided by the potential field force as a mass-spring-damper system, and improving the model; using the numerical integration method, through iterative solution, generating a single-step planning trajectory, and delivering it to the robot's underlying controller for execution. The present invention improves the accuracy, stability and real-time performance of the visual servo tracking control of the neurosurgical robot in a complex clinical environment.

The invention relates to the technical field of three-dimensional environment reconstruction, in particular to a lineation robot working area three-dimensional environment reconstruction system which comprises a point cloud attribute calculation module, a point cloud attribute calculation module, a point cloud attribute calculation module, a point cloud attribute calculation module, a point cloud attribute calculation module and a point cloud attribute calculation module, and the point cloud attribute calculation module is used for conducting ground scanning on a lineation robot working area, obtaining original point cloud and conducting coordinate system alignment on the original point cloud; traversing all the data points and calculating the main curvature value and the main direction value of each data point; according to the method, after ground scanning is carried out on a working area of the scribing robot and an original point cloud is obtained, coordinate system alignment and main curvature and main direction calculation of each data point are carried out, and on the basis of establishing a curvature attribute point cloud with a local geometric attribute, a set threshold value is adopted to screen data points with relatively high main curvature values; the sensitive response capability to fine ground lineation traces is improved; the discrete lineation feature expression with higher spatial continuity and more accurate form recognition is constructed by combining a feature point cluster established by spatial proximity and geometric direction consistency.

The invention provides a differentiable simulation method and device based on three-dimensional Gaussian sputtering and electronic equipment, a three-dimensional Gaussian model is constructed based on image data and attitude data of a working area of a robot, and based on the three-dimensional Gaussian model, the internal motion state of the robot and the action output by a strategy network, the differentiable simulation of the robot is realized. And performing three-dimensional Gaussian sputtering differential rendering to generate a high-fidelity image, and training a strategy network based on the high-fidelity image and the internal motion state of the robot so as to finely adjust the three-dimensional Gaussian model based on the high-fidelity image and the real visual observation image of the robot in the process that the robot moves based on the output action of the strategy network. By the adoption of the method, strategy optimization can be more efficient, the cost and time of simulation environment construction are reduced, and therefore the applicability of the intelligent robot in the complex dynamic environment is further improved.

The invention relates to a device (10) for processing the front and / or rear bumper (28) of a passenger car, comprising a robot (12) which has gripping elements (22, 24, 26) for the bumper (28), comprising a processing station (14) arranged in the working area of ​​the robot (12) for processing the bumper (28), and a detection device (18) which is configured to detect the position of the bumper (28) at least in the area of ​​the at least one processing station (14). The detection device (18) is connected to a controller (16) of the robot (12), wherein the robot (12) is configured to hold the bumper (28) in the processing station (14) during processing and the controller (16) is configured to control the robot (12) to achieve a target position of the bumper (28) in the at least one processing station (14) depending on the signals from the detection device (18).

The application discloses a kind of hybrid flexible robot workspace analysis method and system, the method comprises the following steps: considering minimizing rope length error, constructing the forward kinematics model of hybrid flexible robot, obtaining the end pose of hybrid flexible robot;The inverse kinematics model of hybrid flexible robot is constructed, and the generalized joint angle control quantity of hybrid flexible robot is obtained;The statics analysis model of hybrid flexible robot is constructed, and the static force balance condition of robot is obtained;The end pose of hybrid flexible robot, the generalized joint angle control quantity and static force balance condition are combined, and the workspace of hybrid flexible robot is constructed, and the working state of hybrid flexible robot is controlled according to workspace.It can quickly and accurately complete the task that needs large space movement and local fine operation simultaneously.The application can be widely applied to the technical field of hybrid flexible robot as a kind of hybrid flexible robot workspace analysis method and system.

The present application relates to a kind of flexible cloth manipulation methods based on key point detection, comprising the following steps: step 1, construct dual-arm robot workspace, the workspace includes top depth camera, the cloth state and surrounding environment in the manipulation area are captured by the real-time capture of visual data by the depth camera;Step 2, define multiple action primitives;Step 3, decode multiple action primitives using VIT-Transformer, generate operation strategy, output the action that needs to be executed;Step 4, key point detection and identification are carried out to cloth by Swin-Transformer, select operation strategy, generate action instruction;Step 5, action instruction is sent to dual-arm robot, and action instruction is executed by dual-arm robot.Compared with prior art, the present application can improve operation efficiency, improve adaptability, improve accuracy, reduce implementation cost and the advantages such as wide application.

The invention relates to the technical field of robot navigation, and discloses a path planning method based on fusion of a zero neural network and a resistance network. According to the method, a robot working area is dispersed into a grid map, the mapping relation between the grid map and a time-varying conical curved surface resistance network is established, obstacle grids correspond to disconnection resistors, and a starting point and an ending point correspond to a current injection point and a current outflow point respectively; constructing a fast zero neural network based on the resistance network mathematical model, and solving time-varying potential of each node; starting from the starting point by the robot, screening candidate nodes at the current grid position, determining the next step of moving the grid by combining the time-varying potential and the direction deviation of the candidate nodes, and repeating until the robot arrives at the terminal point. The real-time performance and the dynamic adaptability of path planning are improved, the local optimal solution is effectively avoided, the path planning is stable and reliable, and the method is suitable for various complex operation scenes.

The invention relates to a safeguarding device for the work area (2) of a robot (1), which removes components from several storage containers (6) and uses them to equip a work device (3), against accidentally entering persons (9) by means of at least one protective fence (4) which forms at least a section of the boundary of the work area (2). The safeguarding device according to the invention is characterized in that at least a further section of the boundary of the work area (2) is formed by the storage containers (6) mounted on a base frame (7).