Falling Prediction and Recovery Control for a Humanoid Robot

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.