UBTECH Robotics

Abstract A method for controlling a robotic arm that includes an end effector and a sensor that are mounted at an end of the robotic arm includes: obtaining, by the sensor, n gravity matrix data, wherein the n gravity matrix data are gravity matrix data of the end effector in an end coordinate system when the robotic arm is in a different poses, n≤3; determining n rotation transformation matrices from a base coordinate system of the robotic arm to the end coordinate system when the robotic arm is in n different poses; calculating coordinates of a center of mass and mass of the end effector based on the n gravity matrix data and the a rotation transformation matrices; and controlling the robotic arm based on the coordinates of the center of mass and the mass.

Abstract A robot control method includes: building a two-wheeled inverted pendulum model based on a wheel-legged robot; constructing initial state-space equations based on the two-wheeled inverted pendulum model; linearizing the initial state-space equations to obtain the state-space equations for a linear time-invariant system; obtaining a quadratic performance objective function according to the state-space equations for the linear time-invariant system; and solving the quadratic performance objective function by a linear quadratic regulator to obtain wheel torques of the wheel-legged robot, and controlling the wheel-legged robot according to the wheel torques.

Abstract A mapping method, a computer-readable storage medium, and a robot are provided. The method is applied to a robot including a first lidar and a second lidar, where the first lidar is installed at a position higher than that of the second lidar. The method includes: obtaining a first laser key frame; calculating a current pose of a robot based on the first laser key frame; updating a first probability map based on the current pose and the first laser key frame; obtaining a second laser key frame; updating a second probability map based on the current pose and the second laser key frame; and generating a fused grid map for navigating the robot based on the updated first probability map and the updated second probability map, thereby greatly improving the stability of positioning and navigation.

Abstract A swimming pool cleaning system includes: a pool cleaning robot (10) that is to at least clean a floor and pool walls of a swimming pool; and a surface cleaning device (20) that is to at least clean a pool surface of the swimming pool. The surface cleaning device (20) is wirelessly connectable to an external control device and has a connecting cable (21) that electrically connects the surface cleaning device (20) to the pool cleaning robot (10), so that the pool cleaning robot (10) communicates with the external control device via the surface cleaning device (20).

Abstract A robot joint configuration determining method, a robot using the same, and a computer readable storage medium are provided. The method includes: simulating a joint model of a first joint of the robot using first motion deviation data to obtain first result data; simulating the joint model using second motion deviation data to obtain second result data; taking the motion deviation data corresponding to one of the first result data and the second result data meeting one or more preset conditions as a target motion deviation data for the first joint; and determining type information of a reducer in a configuration information of the first joint based on the target motion deviation data. In the present disclosure, the motion deviation of the first joint that is relatively accurate can be obtained through the results of the two simulations.

Abstract A robot calibration method, a robot, and a computer-readable storage medium are provided. The method includes: obtaining operation space information of the execution end of the robot; obtaining operation space points after gridding an operation space of the robot by gridding the operation space based on the operation space information; obtaining calibration data by controlling the execution end to move to the operation space points meeting a preset requirement; and calibrating the hand and the image detection device of the robot based on the obtained calibration data. In this manner, the operation space points are determined by gridding the operation space based on the operation space information, and the execution end can be automatically controlled to move to the operation space points that meet the preset requirements so as to obtain the calibration data in an automatic and accurate manner, thereby simplifying the calibration process and improving the efficiency.

Abstract A robotic arm angle interval inverse solving method and a robotic arm using the same are provided. The method includes: obtaining a joint angle calculation model and a differential relationship model of a target joint of the robotic arm; obtaining extreme arm angles corresponding to a joint angle of the differential relationship model at extreme values based on the differential relationship model; obtaining a joint arm angle interval corresponding to the target joint based on the extreme arm angle and the joint angle calculation model; and obtaining a target arm angle interval corresponding to the robotic arm based on the joint arm angle interval corresponding to the target joint of the robotic arm. In comparison with the existing method to solve the arm angle interval of the robotic arm, a more accurate arm angle interval can be obtained.

Abstract A person re-identification method, a storage medium, and a terminal device are provided. In the method, a loss function used during model training is a preset distribution-based triplet loss function constraining a difference between a mean of a negative sample feature distance and a mean of a positive sample feature distance to be larger than a preset difference threshold; where the positive sample feature distance is a distance between a feature of a reference image, and a feature of a positive sample image, and the negative sample feature distance is a distance between the feature of the reference image and a feature of a negative sample image. In this manner, it can constrain the mean of the positive sample feature distance and that of the negative sample feature distance, thereby improving the accuracy of person re-identification results.