Online detection of calibration errors in humanoid robots

摘要

One of the reasons why humanoid robots fall is the presence of calibration errors at their joints. In this paper, we propose a method for detecting these calibration errors during online operation. We use a signal classification technique to detect differences in the signal patterns of sensors mounted on humanoids caused by calibration errors. Moreover, we present a method using ℓ- and ℓ-SVM for selecting efficient features for pattern classification. Our analysis results show that our approach correctly classified 84% of the data into normal and abnormal classes.