Mobility is a significant robotic task. It is the most important function when robotics is applied to domains such as autonomous cars, home service robots, and autonomous underwater vehicles. Despite extensive research on this topic, robots still suffer from difficulties when moving in complex environments, especially in practical applications. Therefore, the ability to have enough intelligence while moving is a key issue for the success of robots. Researchers have proposed a variety of methods and algorithms, including navigation and tracking. To help readers swiftly understand the recent advances in methodology and algorithms for robot movement, we present this survey, which provides a detailed review of the existing methods of navigation and tracking. In particular, this survey features a relation-based architecture that enables readers to easily grasp the key points of mobile intelligence. We first outline the key problems in robot systems and point out the relationship among robotics, navigation, and tracking. We then illustrate navigation using different sensors and the fusion methods and detail the state estimation and tracking models for target maneuvering. Finally, we address several issues of deep learning as well as the mobile intelligence of robots as suggested future research topics. The contributions of this survey are threefold. First, we review the literature of navigation according to the applied sensors and fusion method. Second, we detail the models for target maneuvering and the existing tracking based on estimation, such as the Kalman filter and its series developed form, according to their model-construction mechanisms: linear, nonlinear, and non-Gaussian white noise. Third, we illustrate the artificial intelligence approach-especially deep learning methods-and discuss its combination with the estimation method.

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In practical RFID tracking systems, usually it is impossible that the readers are placed right with a "grid" structure, so effective estimation method is required to obtain the accurate trajectory. Due to the data-driven mechanism, measurement of RFID system is sampled irregularly; therefore the traditional recursive estimation may fail from to + 1 sampling point. Moreover, because the distribution density of the readers is nonuniform and multiple measurements might be implemented simultaneously, fusion of estimations also needs to be considered. In this paper, an irregular estimation strategy with parallel structure was developed, where the dynamic model update and states fusion estimation were processed synchronously to achieve real-time indoor RFID tracking. Two nonlinear estimation methods were proposed based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively. The tracking performances were compared, and the simulation results show that the developed UKF method got lower covariance in indoor RFID tracking while the EKF one cost less calculating time.

The storage volume of internet data center is one of the classical time series. It is very valuable to predict the storage volume of a data center for the business value. However, the storage volume series from a data center is always “dirty,” which contains the noise, missing data, and outliers, so it is necessary to extract the main trend of storage volume series for the future prediction processing. In this paper, we propose an irregular sampling estimation method to extract the main trend of the time series, in which the Kalman filter is used to remove the “dirty” data; then the cubic spline interpolation and average method are used to reconstruct the main trend. The developed method is applied in the storage volume series of internet data center. The experiment results show that the developed method can estimate the main trend of storage volume series accurately and make great contribution to predict the future volume value.

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