Estimation of the Trunk Attitude of a Humanoid by Data Fusion of Inertial Sensors and Joint Encoders

摘要

The major problem associated with the walking of humanoid robots is to main- tain its dynamic equilibrium while walking. To achieve this one must detect gait instability during walking to apply proper fall avoidance schemes and bring back the robot into stable equilibrium. A good approach to detect gait insta- bility is to study the evolution of the attitude of the humanoid's trunk. Most attitude estimation techniques involve using the information from inertial sen- sors positioned at the trunk. However, inertial sensors like accelerometer and gyro are highly prone to noise which lead to poor attitude estimates that can cause false fall detections and falsely trigger fall avoidance schemes. In this paper we present a novel way to access the information from joint encoders present in the legs and fuse it with the information from inertial sensors to provide a highly improved attitude estimate during humanoid walk. Also if the joint encoders' attitude measure is compared separately with the IMU's atti- tude estimate, then it is observed that they are different when there is a change of contact between the stance leg and the ground. This may be used to detect a loss of contact and can be verified by the information from force sensors present at the feet of the robot. The propositions are validated by experiments performed on humanoid robot NAO. Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd.