Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

Abstract

In this paper an Extended Kalman Filter (EKF) is used in order to estimate the real state of a humanoid robot (HRP-2 robot in our case study) using the combination of the information coming from the encoders (kinematics) and from the Inertial Measurement Unit (IMU). The integration of the kinematic information into the Kalman filtering process allows a good estimation of the attitude and reduces the complexity of the problem to the use of simple kinematic transformations, even considering the existence of accelerations and mechanical flexibilities in the robot. The EKF estimator presented here is an open solution directly applicable to any humanoid robot, which is the main contribution of our approach. Experimental results are given showing the good performance of the method.Key words: Humanoid robot, Attitude estimation, Kalman filtering, Sensor integration, Kinematics simplicity Otvoreno rješenje za estimaciju položaja humanoidnog robota integracijom senzora i proširenim Kalmanovim filtrom. U ovomečlanku koristi se prošireni Kalmanov filtar (EKF) za estimaciju stanja humanoidnog robota (HRP-2 robot) kombiniranjem informacija iz enkodera (kinematika) i inercijalne mjerne jedinice (IMU). Integracijom kinematičkih informacija u proces Kalmanovog filtra ostvaruje se dobra estimacija položaja i smanjenje složenosti problema jednostavnim kinematičkim transformacijama,čak i u slučaju postojanja akceleracija te mehaničkih savijanja robota. EKF predstavljen u ovomečlanku otvoreno je rješenje primjenjivo na bilo kojem humanoidnom robotu, što je i glavni doprinos predloženog pristupa. Ekperimentalni rezultati pokazuju dobro ponašanje predložene metode.

Cost-effective improvements of a rotating platform by integration of a high-accuracy inclinometer and encoders for attitude evaluation

“…However, the inclinometer is sensitive to vibratory and acceleration disturbances [21]. The optical encoder can measure the gimbal angle directly in a dynamic condition [22], whereas the inclinometer can provide high-accuracy attitude in a static condition; therefore, we developed a low-cost portable platform by integrating these sensors.…”