Ubtech Robotics Corp Ltd

Simin Zhang

Youjun Xiong

Jian Zhang

Jianxin Pang

Zhichao Liu

Hongjian Liu

Yongsheng Zhao

Zhanjia Bi

Chenchen Jiang

Longbiao Bai

Yun Zhao

Jichao Jiao

Jiahao Ji

A collided position determination method, a computer-readable storage medium, and a robot are provided. The method includes: obtaining, from a collision sensor of the robot, a triggered signal corresponding to a collision of the robot; determining at least two candidate positions of the collision based on the triggered signal corresponding to the collision; obtaining a motion trajectory of the robot after the collision; and obtaining a collided position by screening each of the candidate positions of the collision according to the motion trajectory. In this manner, when the robot collides during its movement, the collision signal generated by the collision sensor of the robot can accurately mark the collided position, thereby reducing the probability of the robot colliding again at the same position.

Collided position determination method, computer-readable storage medium, and robot

The present disclosure provides a robot visual image feature extraction method as well as an apparatus and a robot using the same. The method includes: collecting image data through visual sensor(s) of the robot, and collecting angular velocity data through inertial sensor(s) of the robot; calculating a relative pose between image frames in the image data based on the angular velocity data; extracting feature points of the first image frame in the image data; calculating a projection position of each feature point of the k-th image frame in the k+1-th image frame based on a relative pose between the k-th image frame and the k+1-th image frame; and searching for each feature point in the projection position in the k+1-th image frame, and performing a synchronous positioning and a mapping based on the searched feature point. In this manner, the feature points of dynamic objects are eliminated.

Robot vision image feature extraction method and apparatus and robot using the same

The present disclosure relates to robot technology, and particularly to a robot localization method as well as an apparatus and a robot using the same. The method includes: obtaining a set of particles for localizing the robot; updating a position of each particle in the set of particles based on a preset motion model to obtain the updated position of the particle; obtaining laser measurement data and UWB measurement data; calculating a matching probability of each particle based on the laser measurement data, the UWB measurement data, and the updated position of the particle; and localizing the robot based on the matching probability of each particle. In such a manner, the UWB measurement data is applied to the traditional particle filtering localization algorithm based on laser measurement data so as to enhance the localization precision in large indoor scenes.

Robot localization method and apparatus and robot using the same

The present disclosure provides a virtual rail based cruise method as well as an apparatus and a robot using the same. The method includes: obtaining a digital map including a virtual rail; performing a path planning based on the virtual rail, a current position of the robot, and a cruise end point to obtain a cruise path; and obtaining parameter(s) of the robot by calculating through a preset path tracking algorithm based on the cruise path and the current position of the robot, and controlling the robot based on the control parameter(s). In this manner, the problems of the prior art that needs to lay a rail or set an auxiliary device which causes high cost and inconvenience in usage as well as the rail needs to be re-laid or the auxiliary device needs to be reinstalled when the route is to be changed can be solved.

Virtual rail based cruise method and apparatus and robot using the same

The present disclosure provides a robot path planning method as well as an apparatus and a robot using the same. The method includes: obtaining a grid map and obtaining a position of obstacle and a position of track in the grid map; determining a cost of grids of the grid map based on the position of obstacle and the position of track; generating a grid cost map based on the cost of the grids and the grid map; and planning a global path of the robot from a current position to a destination position based on the grid cost map. In this manner, it effectively integrates free navigation and track navigation, thereby improving the flexibility of obstacle avoidance and ensuring the safety of obstacle avoidance of the robot.

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Shenzhen, China

Simin Zhang