Ubtech Robotics Corp Ltd

Ubkang (qingdao) Technology Co., Ltd.

Ubtech North America Research and Development Center Corp

Futronics (na) Corporation

Zhen Xiu

Huan Tan

Dan Shao

Chuqiao Dong

Dejun Guo

ChengKun Zhang

Houzhu Ding

Chengkun Zhang

Brandon Jon LaPlante

Armen Gardabad Ohanian

Francisco Jose Hernandez

Yang Shen

Luis Alfredo Mateos Guzman

Aravind Sreejith

Matthew Keith Xi-Jie Pan

David Ayllón Álvarez

Adam David King

Muhammed Rasid Pac

Human lying posture detections are disclosed. A human lying on a bed is detected by obtaining an image through a depth camera, detecting objects in the image and marking the objects in the image using 2D bounding boxes by deep learning, determining the human being in a lying posture in response to a width and a height of the 2D bounding box of the human meeting a predetermined condition, detecting one or more skin areas in the image and generating skin area 2D bounding boxes to mark each of the one or more skin areas using a skin detection algorithm, and determining the human being in the lying posture in response to the skin area 2D bounding boxes and the 2D bounding box of the bed meeting a predetermined positional relationship.

Human lying posture detection method and mobile machine using the same

A method for facilitating a multiparty conversation is disclosed. An electronic device using the method may facilitate a multiparty conversation by identifying participants of a conversation, localizing relative positions of the participants, detecting speeches of the conversation, matching one of the participants to each of the detected speeches according to the relative positions of the participants, counting participations of the matched participant in the conversation, identifying a passive subject from all the participants according to the participations of all the participants in the conversation, finding a topic of the conversation between the participants, and engaging the passive subject by addressing the passive subject and speaking a sentence related to the topic.

Conversation facilitating method and electronic device using the same

Navigation of a mobility aid robot having a camera and gripping part(s) disposed toward different directions is disclosed. The mobility aid robot is navigated to approach a user by identifying a posture of the user through the camera, determining a mode of the robot according to a type of the specified task to be performed on the user and the identified posture of the user, controlling the robot to move according to a planned trajectory corresponding to the determined mode of the robot, and turning the robot upon reaching the desired pose such that the gripping part faces the user, in response to the determined mode of the robot corresponding to the specified task of an assisting type and the user at one of a standing posture and a sitting posture.

Mobility aid robot navigating method and mobility aid robot using the same

Carpet detection using an RGB-D camera and mobile machine movement control based thereon are disclosed. Carpets and carpet curls are detected by obtaining a RGB-D image pair including an RGB image and a depth image through an RGB-D camera, detecting carpet and carpet-curl areas in the RGB image and generating a 2D bounding box to mark each area using a deep learning model, and generating groups of carpet and carpet-curt points corresponding to each of the carpet and carpet-curl areas by matching each pixel of the RGB image within each 2D bounding box corresponding to the carpet and carpet curl areas to each pixel in the depth image.

Carpet detection method, movement control method, and mobile machine using the same

Response methods to human abnormal behaviors for a mobility aid robot having a user-facing camera are disclosed. The mobility aid robot responds to human abnormal behaviors by detecting a face of a human during the robot aiding the human to move through the camera, comparing an initial size of the face and an immediate size of the face in response to the face of the human having detected during the robot aiding the human to move, determining the human as in abnormal behavior(s) in response to the immediate size of the face being smaller than the initial size of the face, and performing response(s) corresponding to the abnormal behavior(s) in response to the human being in the abnormal behavior(s), where the response(s) include slowing down the robot.

Human abnormal behavior response method and mobility aid robot using the same

A method for controlling a robotic walking assistant that includes a wheeled base having one or more wheels, two handles and a foldable seat that are coupled to the wheeled base, includes: detecting whether two hands of a user have held the two handles of the robotic walking assistant; receiving a command from the user to select an operation mode in response to detection of the two hands holding the two handles; controlling the wheeled base to move in response to a walking assistive mode being selected; providing resistance to at least one of the one or more wheels according to selection of the user, in response to a walking training mode being selected; and locking the one or more wheels in response to a static training mode being selected.

Robotic walking assistant, method for controlling the same and computer-readable storage medium

A human-object scene recognition method includes: acquiring an input RGB image and a depth image corresponding to the RGB image; detecting objects and humans in the RGB image using a segmentation classification algorithm based on a sample database; in response to detection of objects and/or humans, performing a segment detection to each of the detected objects and/or humans based on the ROB image and the depth image, and acquiring a result of the segment detection; calculating 3D hounding boxes for each of the detected objects and/or humans according to the result of the segment detection, and determining a position of each of the detected objects and/or humans according to the 3D bounding boxes.

Human-object scene recognition method, device and computer-readable storage medium

Human posture determination is disclosed. Human posture is determined by obtaining range image(s) through a range camera, detecting key points of an estimated skeleton of a human in color data of the range image(s) and calculating positions of the detected key points based on depth data of the range image(s), choosing a feature map from a set of predefined feature maps based on the detected key points among a set of predefined key points, obtaining two features of a body of the human corresponding to the chosen feature map based on the positions of the detected key points, and determining a posture of the human according to the two features in the chosen feature map.

Human posture determination method and mobile machine using the same

A robotic walking assistant includes a wheeled base having a base and one or more position adjustable wheels connected to the base, a body disposed in a vertical direction, positioned on the wheeled base and having a handle, and a control system that receives command instructions. Each of the one or more wheels is slidable with respect to the base between a retracted position and an extended position in a direction that is substantially parallel to a surface where the wheeled base moves. In response to the command instructions, the control system moves the one or more wheels between the retracted positions and the extended positions.

Robotic walking assistant

On-floor obstacle detection using an RGB-D camera is disclosed. An obstacle on a floor is detected by receiving an image including depth channel data and RGB channel data through the RGB-D camera, estimating a ground plane corresponding to the floor based on the depth channel data, obtaining a foreground of the image corresponding to the ground plane based on the depth channel data, performing a distribution modeling on the foreground of the image based on the RGB channel data to obtain a 2D location of the obstacle, and transforming the 2D location of the obstacle into a 3D location of the obstacle based on the depth channel data.

On-floor obstacle detection method and mobile machine using the same

Robot

A robotic assistant includes a wheeled base, a body positioned on the base, a foldable seat rotatably connected to the body, an actuator to rotate the foldable seat with respect to the body, and a control system that receives command instructions. The actuator is electrically coupled to the control system. In response to the command instructions, the control system is to control the actuator to rotate the foldable seat to a folded position or an unfolded position. The control system is further to detect whether an external force from a user has applied to the foldable seat, and release the actuator to allow the foldable seat to be manually rotated.

Robotic assistant and method for controlling the same

A robotic assistant includes a base; an elevation mechanism positioned on the base; a display rotatably mounted on the elevation mechanism; a camera positioned on the display; and a control system that receives command instructions. In response to the command instructions, the control system is to detect movement of a face of the user in a vertical direction based on the images captured by the camera. In response to detection of the movement of the face of the user in the vertical direction, the control system is to rotate the display and actuate the elevation mechanism to the move the display up and down to allow the camera to face the face of the user during the movement of the face of the user in the vertical direction.

Growing community of inventors

Chino Hills, CA, United States of America

Zhen Xiu