Abstract
In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245 m0.0224 m0.0039 rad]T and the average localization errors using the conventional EKF are [1.0405 m2.2700 m0.1782 rad]T, [0.4963 m0.3482 m0.0254 rad]T and [0.2774 m0.3897 m0.0353 rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.
Highlights
Because of the extensive applications of mobile robots in diverse fields, such as aerospace, intelligent industry and intelligent transportation, and so on, many efforts have been made on the studies of the robot
In a given environment where the associations between landmarks and the Doppler–azimuth radar (D–AR) are known, an extended Kalman filter (EKF) is employed to perform the robot localization in [6], where the D–AR is equipped on the robot platform to output the measurements containing the Doppler frequency shift and the azimuth
Compared with the conventional EKF, three sets of simulations are conducted to testify to the usefulness of the adaptive fading extended Kalman filter (AFEKF)-based localization scheme under three different types of modeling errors
Summary
Because of the extensive applications of mobile robots in diverse fields, such as aerospace, intelligent industry and intelligent transportation, and so on, many efforts have been made on the studies of the robot. As an essential issue and a research hot topic in the robot field, the localization problem has attracted much research attention [1,2]. Various types of external sensors, including inertial sensors [3], LIDAR [4,5], Doppler–azimuth radar [6] (D–AR) and ultrasonic sensors [7], have been used to obtain the measurements. The D–AR with the merits of lower cost, smaller size and lighter weight, has obtained initial attention in the robot localization problem. In a given environment where the associations between landmarks and the D–AR are known, an extended Kalman filter (EKF) is employed to perform the robot localization in [6], where the D–AR is equipped on the robot platform to output the measurements containing the Doppler frequency shift and the azimuth. In Reference [11], regarding a specific case in which the associations between landmarks and the D–AR are unknown, a particle filtering-based localization scheme has been proposed
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