ID number setting method, computer-readable storage medium and modular device

    公开(公告)号:US11919162B2

    公开(公告)日:2024-03-05

    申请号:US17134155

    申请日:2020-12-24

    Inventor: Wei He Youjun Xiong

    CPC classification number: B25J9/1617 A63H33/04 G06F13/4068 G05B2219/40304

    Abstract: An identification (ID) number setting method for a modular device that comprises a master building element and a plurality of slave building elements that are connected to the master building element, includes: disconnecting the slave building elements from the master building element; setting ID numbers of all of the slave building elements to be a preset ID number; and assigning new ID numbers to slave building elements of N tiers that are connected to one output interface of the master building element in an order from first tier to Nth tier, wherein the slave building elements of the first tier are slave building elements that are directly connected to the output interface, the slave building elements of the Nth tier are slave building elements that are indirectly connected to the output interface through slave building elements of a (N−1)th tier, N is a natural number greater than 1.

    MAPPING METHOD FOR ROBOT, ROBOT AND COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20240053168A1

    公开(公告)日:2024-02-15

    申请号:US18232791

    申请日:2023-08-10

    CPC classification number: G01C21/3837

    Abstract: A mapping method for a robot includes: detecting a plurality of linear trajectories of the robot in a process of building a map; inserting a positioning key frame corresponding to each of the linear trajectories, wherein the positioning key frame comprises, when the robot is located on a corresponding one of the linear trajectories, a first pose in a positioning coordinate system, and a second pose in a map coordinate system; and for each two adjacent ones of the linear trajectories, according to one of the first poses determined according to a displacement between the positioning key frames of the two adjacent ones of the linear trajectories, performing optimization of loop closure constraints on the second poses of the positioning key frames, and generating a map based on the optimized positioning key frames.

    Modular device, control method and robot

    公开(公告)号:US11901668B2

    公开(公告)日:2024-02-13

    申请号:US16926634

    申请日:2020-07-10

    Inventor: Wei He Youjun Xiong

    Abstract: A modular device includes a polyhedral building element having a first type connector and a number of second type connectors; and a main control module comprising a plurality of second type connectors. The first type connector and the second type connectors are disposed on side surfaces of the building element. One of the second type connectors of the main control module is used to magnetically connect with the first type connector of the building element so as to detachably connect the building element to the main control module. The first type connector includes a first detection circuit, and each second type connector includes a second detection circuit.

    ROBOT STEP LENGTH CONTROL METHOD, ROBOT CONTROLLER, AND COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20240017404A1

    公开(公告)日:2024-01-18

    申请号:US18371472

    申请日:2023-09-22

    CPC classification number: B25J9/1628

    Abstract: A robot step length control method, a robot controller, and a computer-readable storage medium are provided. The method includes: if it detects that a humanoid robot is not in a balanced state at a current time, it correspondingly obtains a torso deflection posture parameter, a lower limb parameter and a leg swing frequency of the legs of the humanoid robot at the current time; and it calculates, using a swinging leg capture point algorithm, a calculated step length for maintaining a stable state of the humanoid robot that meets a posture balance requirement of the robot at the current time based on the torso deflection posture parameter, the lower limb parameter, and the leg swing frequency, so that the humanoid robot can be restored to the balanced state after moving with the calculated step length, thereby improving the anti-interference ability of the robot.

    DYNAMIC TARGET TRACKING METHOD, ROBOT AND COMPUTER-READABLE STORAGE MEDIUM

    公开(公告)号:US20240009841A1

    公开(公告)日:2024-01-11

    申请号:US18217662

    申请日:2023-07-03

    CPC classification number: B25J9/1664 B25J9/161 B25J17/00

    Abstract: A dynamic target tracking method for a robot having multiple joints includes: obtaining a motion state of a tracked dynamic target in real time; performing motion prediction according to the motion state at a current moment to obtain a predicted position of the dynamic target; performing lag compensation on the predicted position to obtain a compensated predicted position; performing on-line trajectory planning according to the compensated predicted position to obtain planning quantities of multi-step joint motion states at multiple future moments, and determining a multi-step optimization trajectory according to the planning quantities and a multi-step optimization objective function; and controlling the joints of the robot to according to the multi-step optimization trajectory.

    LINKAGE MECHANISM, ROBOTIC FINGER AND ROBOT
    236.
    发明公开

    公开(公告)号:US20230415355A1

    公开(公告)日:2023-12-28

    申请号:US18243669

    申请日:2023-09-08

    CPC classification number: B25J15/0009 B25J15/022

    Abstract: A linkage mechanism includes: a base member; a first link having a first end rotatably connected to the base member; a second link rotatably connected to the first link; a connecting member rotatably connected to the base member and the second link; an actuating mechanism having a linear actuator, a pushing member, and a transmission member, the pushing member slidably connected to the output shaft, the pushing member having a pushing surface, the transmission member including a first end hinged to the pushing member, and a second end pivoted to the first end of the first link. When the output shaft extends to push the pushing surface, the pushing member moves and the first link rotates relative to the base member.

    Action imitation method and robot and computer readable medium using the same

    公开(公告)号:US11850747B2

    公开(公告)日:2023-12-26

    申请号:US17112569

    申请日:2020-12-04

    CPC classification number: B25J9/161 B25J9/1697 B25J19/023 G06N3/08

    Abstract: The present disclosure provides an action imitation method as well as a robot and a computer readable storage medium using the same. The method includes: collecting a plurality of action images of a to-be-imitated object; processing the action images through a pre-trained convolutional neural network to obtain a position coordinate set of position coordinates of a plurality of key points of each of the action images; calculating a rotational angle of each of the linkages of the to-be-imitated object based on the position coordinate sets of the action images; and controlling a robot to move according to the rotational angle of each of the linkages of the to-be-imitated object. In the above-mentioned manner, the rotational angle of each linkage of the to-be-imitated object can be obtained by just analyzing and processing the images collected by an ordinary camera without the help of high-precision depth camera.

    PERSON RE-IDENTIFICATION METHOD, COMPUTER-READABLE STORAGE MEDIUM, AND TERMINAL DEVICE

    公开(公告)号:US20230386244A1

    公开(公告)日:2023-11-30

    申请号:US18078027

    申请日:2022-12-08

    CPC classification number: G06V40/103 G06V10/776 G06V10/761

    Abstract: A person re-identification method, a storage medium, and a terminal device are provided. In the method, a preset ratio-based triplet loss function is used as a loss function during training The ratio-based triplet loss function limits a ratio of a positive sample feature distance to a negative sample feature distance to be less than a preset ratio threshold. The positive sample feature distance is a distance between a reference image feature and a positive sample image feature, and the negative sample feature distance is a distance between the reference image feature and a negative sample image feature. Compared with the existing absolute distance-based triplet loss function, in the case of small inter-class differences and large intra-class differences, the ratio-based triplet loss function can effectively improve the stability of model training, the features extracted by the trained model are more discriminative and robust, thereby improving the accuracy of person re-identification results.

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