Performance Evaluation of Extended Kalman Filtering for Obstacle Avoidance of Mobile Robots

Obstacle avoidance is an essential function for the navigation of mobile robots. Noise filtering improves the measurement accuracy of senors and plays an important role for obstacle avoidance in the applications of mobile robots. This study evaluates the performance of the extended Kalman filtering (EKF) and Kalman filtering (KF) for obstacle avoidance of a two-wheeled mobile robot. EKF is an advanced version of traditional KF for signal processing. EKF is used to deal with non-linear problems that KF can not process properly and usually has better ability of noise tolerance than KF. Due to the non-linearity and unstability of sensoring results, KF has limited performance in the underlying problem. The robot used in this study carries some sonar sensors that acquire signals of obstacles periodically. EKF linearizes the estimation around the current measure using the partial derivatives of the process and measurement functions to obtain estimates of actual measurements even when non-linear relationships exist in the underlying problem. Several experiments of obstacle avoidance are conducted on the two-wheeled mobile robot and the results are analyzed. The results show that EKF provides reliable navigation information better than that from traditional KF.