8 results about "Hand eye calibration" patented technology

Provided is a method for improved hand-eye calibration method based on a structured light camera. The method includes: step 1: establishing a pinhole camera model, and using a depth camera to detect a three-dimensional (3D) coordinate to obtain a physical coordinate of each point in an image coordinate system with a known depth relative to a camera coordinate system; step 2: establishing a Denavit-Hartenberg (DH) model of a robotic arm, and moving the robotic arm to a determined coordinate using inverse kinematics; step 3: collecting n sets of point cloud data, applying depth scaling coefficients to the n sets of point cloud data to perform Singular Value Decomposition (SVD), and solving for an optimal depth scaling coefficient using a Nelder-Mead algorithm; and step 4: completing the hand-eye calibration of the robotic arm based on the solved optimal depth scaling coefficient. The method offers strong operability and robustness.

The invention discloses a complex stacking scene robot disordered grabbing method based on three-dimensional vision, and the method comprises the steps: firstly obtaining an original point cloud through a depth camera, and obtaining a high-quality point cloud after statistics and radius filtering, moving least square smoothing, background plane removal and grid simplification; then, hand-eye calibration of participation eyes inside the camera outside the hand is completed, and alignment of vision and a robot coordinate system is achieved; single workpieces are separated through Euclidean clustering; in the key point extraction stage, geometric significance and super voxel density screening are integrated, weighted three-dimensional SIFT and multi-scale SHOT descriptors are adopted for feature expression, and rough registration and symmetric ICP fine registration are combined to obtain the six-degree-of-freedom pose of a workpiece; and based on the self-adaptive curvature and the force closing score, a grabbing posture is optimized, a collision-free path is planned by using an RRT algorithm, and grabbing is executed. The method does not need pre-modeling, can adapt to shielding, symmetry and density change scenes, is low in deployment cost, is simple to maintain, and is suitable for industrial disordered feeding and discharging and logistics sorting.

A method of calibrating a robotic arm which includes perform iterative eye-in-hand and robot calibration, using a calibrated end of arm camera with known intrinsic parameters and a static target, to obtain eye-in-hand transformations and robotic parameters. The method further uses robotic parameters to estimate pose of end of arm, for eye-to-hand calibration, to obtain eye-to-hand transformations and calculates final error compensation based on the robotic parameters, eye-to-hand transformations, and eye-in-hand transformations. In one embodiment, the method calculates a robot positioning error map function, the robot positioning error map function used to adjust movement parameters for the robotic arm.

An in-situ robot programming method for a hydraulic turbine top cover based on binocular structured light vision is provided, which relates to the field of robot vision-guided intelligent processing technologies. The method performs hand-eye calibration on a binocular structured light camera and a robot to construct a coordinate conversion relationship thereof, collects an annular to-be-processed area of the hydraulic turbine top cover by the binocular structured light camera for joining, and obtains a trajectory and a trajectory plan through point cloud processing algorithms, to thereby achieve a rapid programming for the hydraulic turbine top cover based on guidance of binocular structured light vision. The method adapts to changes in sizes of the hydraulic turbine top cover and take into account real-time performance and robustness, so that impacts of uncertainty factors on the system are greatly weakened, and the method has characteristics of intelligence and high degree of systematization.

The invention discloses a multi-modal massage positioning method and system for human body acupuncture points, and mainly relates to the technical field of data multi-modal fusion. Comprising the steps of collecting multi-modal data of a user and constructing a virtual human body model; human body key points are identified based on the multi-modal data; mapping the three-dimensional key points in a depth camera coordinate system to an RGB image coordinate system through coordinate transformation; predicting positions of key acupuncture points of the human body by using a machine learning algorithm; the predicted acupuncture point coordinates are converted into a robot coordinate system through hand-eye calibration; recognizing a massage intention of the user based on a multi-modal intention understanding algorithm; and finally outputting the massage position information to the massage robot to execute massage operation. The intelligent massage robot has the beneficial effects that accurate positioning, multi-modal natural interaction and intelligent intention understanding of human body acupuncture points are realized, and the positioning accuracy and the use convenience of the massage robot are remarkably improved.

The present application relates to a kind of robot kinematics parameter calibration method based on visual multi-point pose constraint, comprising: control end installation camera's robot to be calibrated around the center of calibration board motion, collect robot model data and the nominal pose data of calibration board in multiple pose state during motion;Using hand-eye calibration algorithm obtains initial hand-eye conversion matrix as iteration initial value;Multiple calibration points on calibration board are combined to construct and generate multiple-point pose constraint equation group from the combination of differential pose constraint equation group;The differential pose constraint equation group of single calibration point is the deviation of the nominal pose and actual pose of this calibration point and the relationship of robot kinematics parameter deviation;Multiple-point pose constraint equation group is solved iteratively using weighted least square method, to achieve the robot kinematics parameter deviation obtained when iteration stop relationship is carried out to the kinematics parameter of the robot to be calibrated;It is low in cost, equipment installation precision requirement is low, and actual position of calibration board is not required in calibration process.

A method of calibrating a robotic arm which includes perform iterative eye-in-hand and robot calibration, using a calibrated end of arm camera with known intrinsic parameters and a static target, to obtain eye-in-hand transformations and robotic parameters. The method further uses robotic parameters to estimate pose of end of arm, for eye-to-hand calibration, to obtain eye-to-hand transformations and calculates final error compensation based on the robotic parameters, eye-to-hand transformations, and eye-in-hand transformations. In one embodiment, the method calculates a robot positioning error map function, the robot positioning error map function used to adjust movement parameters for the robotic arm.

This invention discloses a robotic arm grasping control method, device, electronic equipment, and storage medium. The device is applied to a robot, which includes a lifting mechanism, a motor base, an adjustment mechanism, a gripping mechanism, and a positioning camera. The top end of the lifting mechanism is fixedly connected to the bottom end of the motor base, the top end of the motor base is fixedly connected to the bottom end of the adjustment mechanism, one end of the adjustment mechanism is fixedly connected to one end of the gripping mechanism, and one side of the motor base is fixedly connected to one side of the positioning camera. This invention's device employs visual 3D positioning and hand-eye calibration, resulting in small positioning errors and adaptability to various placement postures and irregularly shaped targets. It features a dual closed-loop force control mode, balancing grasping stability and object safety, and is adaptable to various materials and objects. Its modular design allows for independent use or integration into multi-machine collaborative systems, enabling rapid actuator replacement and broad scenario coverage. Full-process anomaly monitoring and fault-tolerant protection prevent collisions, slippage, and object damage, ensuring high operational reliability.