Multi-sensor based fall prediction method for humanoid robots

摘要

This paper proposes a multi-sensor based method to predict the falling of the humanoid in a reliable and agile manner. The fusion of multisensors such as an inertial measurement unit and foot pressure sensors are considered, which can be regarded as human's vestibular and proprioception. We define a set of feature-based fall indicator variables (FIVs) with manually extracted thresholds for four major disturbance scenarios, which are incorporated with an online threshold interpolation technique to manage generic disturbances. Indeed, a general falling is predicted by using a normalized value of the instantaneous and cumulative sum of each FIVs compared to a predefined set-value of the falling indication. The proposed method is evaluated by numerical experiments under 36 different scenarios, involving random disturbances applied at distinct heights. The results depict that the developed method is generic in terms of handling disturbances as well as different configurations of the robot and the use of fused FIVs performs better than that of a single FIV; in particular, the fusion with the foot pressure sensor based indicator increases the overall performance of the prediction.