The micro-electro-mechanical-system (MEMS) inertial measurement unit (IMU) has been widely used in the field of inertial navigation due to its small size, low cost, and light weight, but aligning MEMS IMUs remains a challenge for researchers. MEMS IMUs have been conventionally aligned on a static base, requiring other sensors, such as magnetometers or satellites, to provide auxiliary information, which limits its application range to some extent. Therefore, improving the alignment accuracy of MEMS IMU as much as possible under swing conditions is of considerable value. This paper proposes an alignment method based on the rotation modulation technique (RMT), which is completely self-aligned, unlike the existing alignment techniques. The effect of the inertial sensor errors is mitigated by rotating the IMU. Then, inertial frame-based alignment using the rotation modulation technique (RMT-IFBA) achieved coarse alignment on the swing base. The strong tracking filter (STF) further improved the alignment accuracy. The performance of the proposed method was validated with a physical experiment, and the results of the alignment showed that the standard deviations of pitch, roll, and heading angle were 0.0140 ° , 0.0097 ° , and 0.91 ° , respectively, which verified the practicality and efficacy of the proposed method for the self-alignment of the MEMS IMU on a swing base.

Self-Alignment MEMS IMU Method Based on the Rotation Modulation Technique on a Swing Base

As a typical navigation system, the strapdown inertial navigation system (SINS) is crucial for autonomous underwater vehicles (AUVs) since the SINS accuracy determines the performance of AUVs. Initial alignment is one of the key technologies in SINS, and initial alignment time and initial alignment accuracy affect the performance of SINS directly. As actual systems are nonlinear, the nonlinear filter is widely used to improve the accuracy of the initial alignment. Due to its higher precision and lower computational load, the cubature Kalman filter (CKF) has done well in state estimation. However, the noise characteristics need to be known exactly as prior knowledge, which is difficult or even impossible to achieve. Thus, the adaptive filter should be introduced in the initial alignment algorithm to suppress the uncertainty effect caused by the unknown system noise. Therefore, taking the nonlinearity and uncertainty into account, a novel initial alignment algorithm for AUVs is proposed in this manuscript, based on CKF and the adaptive variance components estimation (VCE) filter (VCKF). Additionally, the simulation and experiment results show that not only the accuracy, but also the convergence speed can be improved with this proposed method. The validity and superiority of this novel adaptive initial alignment algorithm based on VCKF are verified.

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An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

In the future, a pedestrian navigation system (PNS) will be widely applied. In navigation system, an initial alignment is essential before starting navigation. There are two primary alignment methods, one is an inertial/magnetometer assisted alignment method, the other is a multiposition rotation modulation alignment method. However, magnetometer is susceptible to magnetic disturbance, and the large volume of rotary modulation system is not suitable for PNS. To solve these problems, a compound navigation system (CPNS) is proposed. It consists of a single-axis fiber-optic gyro (FOG) and a triaxial microelectromechanical system inertial measurement unit (MIMU) without modulation mechanism. CPNS is placed in different positions. According to the variation law of cosine function, FOG data in multiposition are used as fitting alignment, MEMS gyro tracks attitude changes between different positions. The experimental results demonstrate that initial alignment accuracy can be well calculated with several multiposition schemes under different conditions. This method could be a novel solution for PNS’s initial alignment.

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A Multiposition Initial Alignment Method of Portable MIMU/FOG Compound Navigation System

The MEMS inertial measurement system can not perform the initial self-alignment due to the low measurement precision of MEMS gyro. To solve this problem, an initial alignment method assisted by magnetometers is proposed in this paper. Measurement values of MEMS inertial devices and the heading angle are fused to estimate state variables by using unscented Kalman filtering. Error equations of the MEMS inertial measurement system are modified to satisfy the unscented Kalman filtering model in this paper. Simulation results show that the initial alignment method proposed in this paper satisfies the requirement of the MEMS inertial measurement system which is used in low and medium precision field.

Initial alignment of the MEMS inertial measurement system assisted by magnetometers

As the key technology of intelligent welding, seam tracking plays a vital role in the welding process. The development and application of visual sensing technology has laid the foundation for obtaining accurate weld position information. The intelligent combination of visual sensing and welding robots improves the stability and accuracy of welding seam tracking technology. This paper introduces the composition and principle of the welding seam tracking system, and the classification of visual sensing technology. This paper summarizes and analyses the welding seam image processing method and the research status of robot tracking control technology around the process of welding seam tracking. This paper summarizes the current problems of weld tracking technology and looks forward to the future development trend.

Zhiwei Hu

Papers

Initial alignment of the MEMS inertial measurement system assisted by magnetometers

Research Progress of Robot Vision Sensing Weld Seam Tracking Technology