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Aiming at the low-cost attitude measurement of Unmanned Aerial Vehicle (UAV), a micro-inertial attitude measurement system that comprises of low-precision Micro-Electro Measurement System (MEMS) gyroscopes and accelerometers is designed. The system does not rely on any external aiding information, a stable and reliable attitude measurement information can be achieved by only using the tri-axial gyroscope and the tri-axial accelerometers in various dynamic conditions. Meanwhile, an enhanced Mahony complementary filter algorithm based on gravity field adaptation is proposed to adjust the parameters of filter algorithm automatically. In addition, the quaternion is used to calculate the attitude angles of UAV, which improved the accuracy and reliability of the attitude measurement system in some extent. Furthermore, the feasibility of the UAV attitude measurement system is verified by the helicopter flight experiment. Finally, the experimental results demonstrate that the errors of horizontal attitude angles are less than 3 degrees when they compared with the high-precision fiber optic gyroscope based strapdown inertial navigation system.

To improve the transfer alignment precision and reduce the alignment time of Strapdown Inertial Navigation System (SINS) for fast-response and maneuvering launching of vehicular launching system are of great importance. In this paper, a novel fast and high-precision transfer alignment algorithm is designed. The “speed + attitude” matching model is used for transfer alignment filter measurement updates, and meanwhile its lever-arm error is compensated and eliminated through the direct calculation. In addition, an embedded hardware platform of transfer alignment information solution system is constructed based on the FPGA+DSP processor. Finally, in order to verify the performances of the transfer alignment algorithm, a vehicular launching system is used as the carrier, and the launching arm is erected as the transfer alignment maneuvering condition. The results shown that the azimuth misalignment error estimated by transfer alignment system is less than 3’, and the errors of pitch and roll misalignment angles are less than 2’ within 10s launching arm maneuvering condition, which proves the effectiveness and viability of the proposed algorithm.

In this paper, a method of solving inverse kinematics based on fuzzy algorithm for hybrid boarding system is presented. This approach could actively compensate the ocean wave disturbances when the hybrid mechanism is conducting a certain task, such as, transporting the repairmen into wind turbine platform. By deriving the Jacobian matrix of the hybrid mechanism, the relationship between the angular and linear velocities of variables in joint space and gangway's end-effector in task-space can be obtained. Thereafter, the numerical integral method is used to obtain the inverse kinematic solution. Structurally, the hybrid mechanism consists of a 6-DOFs Stewart parallel platform and a 3-DOFs gangway. Due to the kinematical redundant of the hybrid boarding system, its inverse kinematic solutions are not unique. So a fuzzy strategy is put forward to plan the variable values reasonably. Moreover, it can satisfy the requirement of variable constraints. When conducting ocean wave compensation, specifically, the Stewart parallel platform is used to compensate the wave disturbance in roll direction. Moreover, it is efficient to compensate other disturbances by adjusting the joint angles of gangway. The effectiveness and the robustness of the proposed method is demonstrated using numerical simulation when the wave disturbance is added into the hybrid boarding system.

The interferometric fiber optic gyro (IFOG) that is inside the gyrocompass has been gradually equipped on the marine vessel and the vessel's swing is sensed by IFOG. This will affect the detection of Allan variance coefficients of IFOG while the vessel is in moorage especially in temperature variation conditions. However, the standard IFOG Allan variance noise analysis method is computationally expensive and requires massive data storage which is inappropriate for Allan variance coefficients real-time detection. In addition, for long-voyage and long-endurance requirements of the vessel in the marine environments, the ambient temperature variation causes errors that will degenerate IFOG accuracy. The vessel needs more independence including the fault diagnosis system and reconfiguration of the IFOG gyrocompass. To meet the marine vessel autonomy need, we propose a method for Allan variance coefficients detection including rate ramp R, rate stochastic walk K, bias instability B, angular stochastic walk N and quantization noise Q. In the study, we first eliminate the angular increments of vessel attitude by gravity in inertial frame in the moorage condition, and then realize the Allan variance coefficients real-time detection by adopting nonlinear adaptive Kalman filter with proposed state-space model. Simulations show that the proposed method potentially could implement the Allan variance coefficients’ real-time detection, and estimate the coefficients’ temperature variation effectively and correctly.

Vehicles have to rely on satellite navigation in an open environment. However, satellite navigation cannot obtain accurate positioning information for vehicles in the interior of underground parking lots, as they comprise a semi-enclosed navigation space. Therefore, vehicular navigation needs to take into consideration both outdoor and indoor environments. Actually, outdoor navigation and indoor navigation require different positioning methods, and it is of great importance to choose a reasonable navigation and positioning algorithm solution for vehicles. Fortunately, the integrated navigation of the Global Positioning System (GPS) and the Micro-Electro-Mechanical System (MEMS) inertial navigation system could solve the problem of switching navigation algorithms in the entrance and exit of underground parking lots. This paper proposes a low cost vehicular seamless navigation technology based on the reduced inertial sensor system (RISS)/GPS between the outdoors and an underground garage. Specifically, the enhanced RISS is a positioning algorithm based on three inertial sensors and one odometer, which could achieve a similar location effect as the full model integrated navigation, reduce the costs greatly, and improve the efficiency of each sensor.

With the rapid development of the optic gyrocompass in aerospace and marine navigation systems, as well as the characteristics of the long-running navigation system which are utilized in this study has created unique developments in the field of marine navigation. It is necessary to re-calibrate the inertial measurement unit (IMU) parameters for maintaining the accuracy and reliability of the system. However, the conventional system re-calibration method needs to disassemble the system and move back to the indoor laboratory with high precision turntable, which is costly and has a heavy workload. This study proposes a novel calibration method by using the optimal artificial fish swarm algorithm (AFSA) on a vehicle aided by a computer. The algorithmic principle is described in detail, and the results obtained from both the simulation and the navigation experiments show the proposed calibration method can meet the calibration accuracy and reliability requirements of the marine gyrocompass.

When the Pipeline Inspection Gauge (PIG) is driven by the pressure differential between its two ends, the motion of the PIG inside the inspected pipeline is mainly dominated by the rotation motion around its longitudinal axis (rolling) and the conventional travelling motion along the pipeline longitudinal direction. The rolling motion improves PIG's capability to go through some heavy sludge or wax obstacle areas in the bottom part of the pipeline and enhances the inspection capability for the potential defects inside the pipeline. Moreover, it has some positive effects on the Strapdown Inertial Navigation System (SINS) error model and the corresponding localization precision. This paper presents an innovative error modeling technique, which is based on the detailed analysis of the Micro-Electro-Mechanical System (MEMS) inertial sensors main error sources (gyroscope biases and scale factor errors, accelerometer biases and scale factor errors) on SINS error model when the PIG is exhibiting the rolling motion. The developed error models were verified experimentally at different rolling rates. Results showed high consistency with the theoretical analysis conclusions.

Micro-Electro-Mechanical System (MEMS) based Strapdown Inertial Navigation System (SINS) is preferred for pipeline surveying as it has several priorities including low-cost, small-size, light-weight, and low-power consumption. It acts as a significant tool for localization on potential pipeline defects in pipeline inspection and repair missions. Moreover, it provides measurements to calculate the pipeline centerline coordinates and the horizontal and vertical pipeline curvatures. However, its precision degrades significantly in small diameter pipeline surveying application with both continuous 3D velocity updates and discrete coordinate updates. Attitude updates are an enhanced technique for SINS errors correction in pipeline surveying system. The azimuth and pitch angles of Pipeline Inspection Gauge (PIG) are constant within each straight pipeline segments. This paper introduces a novel pipeline junction detection method by analyzing low-cost MEMS accelerometer measurement data using Fast Orthogonal Search (FOS). The simulated pipeline surveying data sets are obtained in a laboratory environment by Inertial Measurement Unit (IMU), which is mounted on three-axis positioning and rate table. The accelerometer measurement data are extracted and analyzed by FOS. The detection result demonstrated that FOS can detect the pipeline junction with accelerometer measurement data successfully. The FOS pipeline junction detection result provided accurate indication for azimuth and pitch angles measurement updates in SINS at each straight pipeline segment. Moreover, the detection result is expected to provide significant improvement for the MEMS SINS pipeline surveying system precision with reduced cost.

To better address the temperature interference problem of fiber Bragg grating (FBG) strain-based anemometer sensors, based on the FBG sensor theory, the cross-sensitivity mechanism of the fiber grating during wind speed and temperature measurement is analyzed, and the relations between the central wavelength variation of dual Bragg gratings and that of the temperature and wind speed are derived. Two Bragg gratings with different periods are created in the same fiber for wind speed and temperature measurements. The temperature detection grating is encapsulated in a stainless-steel capillary tube single-end fixed support structure to shield the device from wind pressure, achieve a rapid thermal response, and improve the real-time performance of wind speed compensation. To further improve the wind speed temperature compensation results, modification of the surface of the temperature detection grating is proposed via an aluminium coating with a high linear thermal expansion coefficient to improve the temperature detection resolution and thus improve the compensation precision. Additionally, to improve the stability of the anemometer sensor in a wind field, a concave windward baffle structure is proposed. Flow field simulation via the ANSYS software FLUENT shows that this structure is superior to the existing plate structure. The anemometer sensor is evaluated using temperature detection and wind pressure simulation experiments, and the results are as follows: the wind speed detection range is 0 to 35 m / s, the linear sensitivity is 2.4261 × 10 − 1 pm / v2, the temperature detection range is 0°C to 50°C, and the linear sensitivity of the temperature detection grating with aluminium coating modification is increased to 33.9 pm / ° C. In the wind speed range of 0 to 25.4 m / s, the maximum error after temperature compensation is 0.39 m / s.

In order to solve the problems that the fiber optic gyroscope (FOG) error coefficients such as scale factor, installation error and bias are not stable in the calibration of strap-down inertial navigation system (SINS) caused by the angular position error of turntable and the turntable axis mutual misalignment error. Furthermore, an expensive and high precision turntable is commonly required to calibrate the parameters of inertial measurement unit (IMU). For the purpose of calibration, this paper proposes a FOG calibration method to solve these difficulties by using the artificial fish swarm algorithms (AFSA) based on different parameter type. The feasibility of the method is verified by simulation, and the experiment is operated by the FOG SINS, which is self-developed by our laboratory. The result shows that the AFSA is valid, feasible, both saving time, workload and costs for the error parameters calibration of FOG in SINS.