Robust Sensor Based Navigation for Autonomous Mobile Robot

This paper describes a new approach for mobile robot navigation using an interval analysis based adaptive mechanism for an Unscented Kalman filter. The robot is equipped with inertial sensors, encoders and ultrasonic sensors. The map used for this study is two-dimensional and it is assumed to be known a-priori. Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. An Unscented Kalman Filter (UKF) is used here to estimate the robots position using the inertial sensors and encoders. Since the UKF estimates are affected by bias, drift etc, we propose an adaptive mechanism using interval analysis with ultrasonic sensors to correct these defects in estimates. Interval analysis has been already successfully used in the past for robot localisation using time of flight sensors. But this IA algorithm has been extended to incorporate the sensor range limitation as in many real world sensors such as ultrasonic sensors. One of the problems of the use of interval analysis sensor based navigation and localisation is that it can be applicable only in the presence of land marks. This problem is overcome here using additional sensors such as encoders and inertial sensors, which gives an estimate of the robot position using an Unscented Kalman filter in the absence of land marks. In the presence of land marks the complementary robot position information from the Interval analysis algorithm using ultrasonic sensors is used to estimate and bound the errors in the UKF robot position estimate.