The MEMS gyroscope by means of oven control, or the so-called ovenized MEMS gyroscope, isolates the die from the external environment and utilizes a closed-loop temperature control system. It is thought to be important as it does not produce bias, scale factor drift made by thermal variations. Hence, a better temperature stability of the device is expected, which potentially benefits greatly the next generation of high-performance MEMS sensor. Herein, we attempt to provide a comprehensive review of many areas of the ovenized gyros, consisting of principle, key technology, the state-of-the-art results and suggestions. The thermal error mechanism and the importance of temperature control are introduced. The descriptions of the principles and characteristics of both on-chip and off-chip architectures are provided. To suppress heat dissipation at the package level, thermal resistance models based on conduction, convection, and radiation are established and analyzed. In terms of heating drive energy efficiency, comparisons have been made within VCCS (Voltage-Controlled Current Source), V-I (Voltage-to-Current) converters and PWM (Pulse Width Modulation), followed by the analysis of SSP (Single Set Point) and MSP (Multiple Set Point) algorithms. With regards the feedback control system, a systematic discussion on its common temperature detection methods, including CMOS PTAT (Proportional To Absolute Temperature), RTD (Resistance Temperature Detector) and TCF (Temperature Coefficient of Frequency) is presented. The control algorithms and limitations for the temperature control are also presented. At the end, we compared and discussed the most representative ovenized gyros to date, followed by suggestions to future development trends.

The rate-integrating gyroscope (RIG) operation is considered as the next generation architecture for hemispherical resonator gyroscopes (HRGs) with advantages of direct angle measurement and unlimited dynamic range. However, this RIG operation requires high symmetry for the HRG device and the damping mismatch of the two gyroscopic modes will result in a dead area problem. This work analyzes the error mechanism of the damping asymmetry induced dead area and proposed a novel virtual procession compensation method for HRG RIG. The simulation proves the existence of the dead area as the theory predicted. More importantly, the experimental HRG RIG platform with the proposed compensation method can significantly expand the dynamic range with accurate angle measurement and overcome the problem of dead area. The earth rotation is accurate measured which is the first time that captured by a RIG scheme as a state-of-the-art result.

Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This paper provides a new perspective on a deterministic solution to two major branches of HEC problems of forms AX = XB and AX = YB. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm.

Accurate prediction of mesoscale eddy trajectories requires efficient models with large-size available data instances to capture the main eddy characteristics. However, there is a lack of salient attention mechanisms that can recognize the demand of extracting the aggregated features over multidimensional eddy data sources. Additionally, deep learning techniques are very important for eddy trajectory prediction to dynamically capture properties of mesoscale eddies in the South China Sea. In this article, we propose a spatio–temporal attention-based deep learning framework that can orchestrate heterogeneous data integration and propagation trajectory forecast together. It consists of a novel autoencoder equipped with channel and spatial attention mechanisms (CSA-encoder), and a gated recurrent unit (GRU) network with temporal attention layer (TA-GRU). CSA-encoder compresses stereoscopic eddy data with convolutional layers and generates the small-scale and high-quality dataset as the input of TA-GRU. The finer grained TA-GRU method is extended to accurately predict eddy trajectories with more valuable imagery information so that the temporal attention mechanism can automatically select relevant regions within the next 14 days. Our cross-validation results demonstrate that our framework averagely achieves a lower distance error (9 km) and 54% performance improvement over the baseline GRU technique in the next one day, and outperforms two state-of-the-art techniques of long short-term memory and recurrent neural network by 54.9% and 65.6%, respectively.

This work presents a novel framework, named MesoGRU, to accurately predict trajectories of mesoscale eddies (MEs), which is beneficial to study the natural ocean phenomena and perform statistical analysis of oceanic data. MesoGRU first extracts ME trajectory data and establishes two datasets according to the information of location and date in the South China Sea (SCS). Then it effectively processes SCS trajectory data and integrates SLA data and AVISO data into our combined ME dataset (CDME). Furthermore, a designated neural network with a new loss function [(weighted mean square estimation (WMSE)] is designed to learn the characteristics of ME trajectories. After thoroughly analyzing correlations of every two features, the MesoGRU network iteratively extracts key ME features and renormalizes the future time sequence of eddy trajectories. Our verification results demonstrate that MesoGRU has conducted a significant prediction improvement. The mean daily center error of ME trajectory prediction with our scheme is about 8 km and its center error for seven-day forecasting is approximately 18.2 km. Moreover, MesoGRU achieves a higher matching ratio of ME trajectory forecasting compared to other deep learning methods with a single dataset, indicating MesoGRU can predict trajectories as a state-of-the-art method.