Falling Prediction and Recovery Control for a Humanoid Robot

Yang, Tianqi1; Zhang, Weimin1,3; Yu, Zhangguo1,2; Meng, Libo1; Fu, Chenglong4; Huang, Qiang1,2

10.1109/HUMANOIDS.2018.8625000

2018

21640580

978-1-5386-7284-6

2018 IEEE-RAS 18TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS)

2018-November

1073-1079

6-9 Nov. 2018

1 Zhongguancun South Street, Haidian District, Beijing, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

It is very easy for biped robots to fall down. Some previous studies have been carried out to detect the fall state and protect the robot from damage. But it is not enough to detect a fall. It is very important for the biped robot to predict whether it will fall in the future based on the current state. In this paper, we consider a fall state predicted problem for bipedal robots. Based on the D'Alembert principle, this method can predict the fall state at the moment the biped robot deviates from the normal state in every conditions such as standing and walking. It can give the robot more time to recover from the unstable state or protect itself from damage. And its stable control strategy matching the proposed method is also proposed to protect the robot from falling. The result is verified via simulations.

Legged locomotion Robot kinematics Robot sensing systems Foot Collision avoidance Stability analysis

其他

英语

CPCI-S ; EI

111 Project[B08043]

Robotics

Robotics

WOS:000458689700153

20191006589533

Forecasting

Robotics:731.5

Web of Science

1.Beijing Inst Technol, Intelligent Robot Inst, Sch Mechatron Engn, Beijing 100081, Peoples R China
2.Beijing Inst Technol, Minist Educ, Key Lab Biomimet Robots & Syst, Beijing 100081, Peoples R China
3.Beijing Inst Technol, Minist Educ, Int Joint Res Lab Biomimet Robots & Syst, Beijing 100081, Peoples R China
4.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Guangdong, Peoples R China

Yang, Tianqi,Zhang, Weimin,Yu, Zhangguo,et al. Falling Prediction and Recovery Control for a Humanoid Robot[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:1073-1079.