Odometry using wheel encoders provides fundamental pose estimates for wheeled mobile robots. Systematic errors of odometry can be reduced by the calibration of kinematic parameters. The UMBmark method is one of the widely used calibration schemes for two wheel differential mobile robot. In this paper, an accurate calibration scheme of kinematic parameters is proposed by extending the conventional UMBmark. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that remarkably reduce the systematic error of odometry. The new equations were derived to overcome the limitation of the conventional schemes. The second contribution is to propose the design guideline of the test track for calibration experiments. The calibration performance can be significantly improved by appropriate design of the test track. The numerical simulations and experimental results show that the odometry accuracy can be improved by the proposed calibration schemes.

In this paper, we consider the stability analysis of large-scale distributed networked control systems with random communication delays between linearly interconnected subsystems. The stability analysis is performed in the Markov jump linear system framework. There have been considerable researches on stability analysis of Markov jump systems, however, these methods are not applicable to large-scale systems because large numbers of subsystems result in an extremely large number of the switching modes. To avoid this scalability issue, we propose a new reduced mode model for stability analysis, which is computationally efficient. We also consider the case in which the transition probabilities for the Markov jump process contain uncertainties. We provide a new method that estimates bounds for uncertain Markov transition probability matrix to guarantee the system stability. The efficiency and the usefulness of the proposed methods are verified through examples.

This paper focuses on the performance and the robustness analysis of stochastic jump linear systems. The state trajectory under stochastic jump process becomes random variables, which brings forth the probability distributions in the system state. Therefore, we need to adopt a proper metric to measure the system performance with respect to stochastic switching. In this perspective, Wasserstein metric that assesses the distance between probability density functions is applied to provide the performance and the robustness analysis. Both the transient and steady-state performance of the systems with given initial state uncertainties can be measured in this framework. Also, we prove that the convergence of this metric implies the mean square stability. Overall, this study provides a unifying framework for the performance and the robustness analysis of general stochastic jump linear systems, but not necessarily Markovian jump process that is commonly used for stochastic switching. The practical usefulness and efficiency of the proposed method are verified through numerical examples.

Recently, automatic parking assist systems have become commercially available in some cars. In order to improve the reliability and accuracy of parking control, pose estimation problem needs to be solved. Odometry is widely used for pose estimation of a mobile robot. However, most previous odometry calibration methods have focused on two wheeled mobile robots. In this paper, we consider systematic error sources of the Car-Like Mobile Robot(CLMR), and we suggest a useful calibration method for systematic errors. Finally, our calibration method is verified by experiments using a miniature car.

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