STATE ESTIMATION FOR LEGGED ROBOT

Organization Name

TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

Inventor(s)

Yanming Wu of Shenzhen (CN)

Wanchao Chi of Shenzhen (CN)

Haitao Wang of Shenzhen (CN)

Xinyang Jiang of Shenzhen (CN)

Shenghao Zhang of Shenzhen (CN)

Yu Zheng of Shenzhen (CN)

STATE ESTIMATION FOR LEGGED ROBOT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18612974 titled 'STATE ESTIMATION FOR LEGGED ROBOT

Simplified Explanation

This patent application describes a method for estimating the state of a legged robot using sensor information and Kalman filters.

First and second sensor information of the robot are received.
A first Kalman filter is used to determine the first state information of the robot over a period of time based on the sensor information.
Third sensor information is received.
A second Kalman filter is used to determine the second state information of the robot based on the third sensor information and the first state information.
The first state information at the current time is updated based on the second state information to determine the state information of the robot at the current time.

Key Features and Innovation

Utilizes Kalman filters for state estimation in a legged robot.
Incorporates multiple sensor inputs for accurate state determination.
Updates state information in real-time for improved performance.

Potential Applications

Robotics
Autonomous navigation systems
Industrial automation

Problems Solved

Enhances accuracy of state estimation in legged robots.
Improves real-time decision-making capabilities.
Increases overall efficiency and performance of the robot.

Benefits

Enhanced control and stability of legged robots.
Improved navigation and obstacle avoidance.
Increased reliability and safety in robotic operations.

Commercial Applications

Manufacturing industry for automated processes.
Robotics research and development.
Defense and security for surveillance and reconnaissance systems.

Questions about Legged Robot State Estimation

How does the use of Kalman filters improve state estimation in legged robots?

Kalman filters help in combining noisy sensor data to provide a more accurate estimation of the robot's state over time.

What are the advantages of updating state information in real-time for legged robots?

Real-time updates allow for quicker response to changing environments, leading to improved performance and adaptability.

Original Abstract Submitted

In a state estimation method for a legged robot, first sensor information and second sensor information of the legged robot are received. First state information of the legged robot for a period of time is determined, via a first Kalman filter, based on the first sensor information and the second sensor information. Third sensor information of the legged robot is received. Second state information of the legged robot is determined, via a second Kalman filter, based on the third sensor information and the first state information for the period of time. First state information of the legged robot at a current time is updated based on the second state information of the legged robot, to determine state information of the legged robot at the current time.