RoboWalk Multiple-Sensor and Multipolar Data Fusion

Abstract

RoboWalk is a two-legged rehabilitation assisting robot, which helps users by carrying a portion of the weight. In this paper, we propose an algorithm to correct the attitude and position error by means of an Adaptive Root Square Unscented Kalman Filter. To this end, the IMU and encoders are used to determine the robot kinematic configuration, but since there is no solid connection between the ground and the feet, the sensor's data may not lead to exact positioning configuration. Therefore, a new approach for estimating the position of the feet and the seat is presented. The method is based on using the standing foot IMU, and related encoders to obtain the seat position. Then, the obtained result is compared to the seat IMU and correcting the position. Next, the corrected position and the swing leg encoders are used to obtain the swing foot position, which is compared to the corresponding IMU, to modify and yield all corrected positions. Describing the proposed method, it is then implemented, and the results will be discussed.