Instability Detection and Fall Avoidance for a Humanoid using Attitude Sensors and Reflexes

Abstract

Humanoid robots are inherently unstable because their center of mass is high, compared to the support polygon's size. Bipedal walking currently works well only under controlled conditions with limited external disturbances. In less controlled dynamic environments, such as RoboCup soccer fields, external disturbances might be large. While some disturbances might be too large to prevent a fall, some disturbances can be dealt with by specific rescue behaviors. This paper proposes a method to detect instabilities that occur during omnidirectional walking. We model the readings of attitude sensors using sinusoids. The model takes the gait target vector into account. We estimate model parameters from a gait test sequence and detect deviations of the actual sensor readings from the model later on. These deviations are aggregated to an instability indicator that triggers one of two reflexes, based on indicator strength. For small instabilities the robot is slowing down, but continues walking. For stronger instabilities the robot stops and is brought into a stable posture with a low center of mass. Walking continues as soon as the instability disappears. We extensively evaluated our approach in simulation by disturbing the robot with a variety of impulses. The results indicate that our method is very effective. For smaller disturbances, the probability of a fall could be reduced to zero. Most of the medium-sized disturbances could also be rejected. For the evaluation with the real robot, we used a walking against a wall with different speeds and at various angles. Here the results show a similar outcome to the ones in the simulations