An Innovative Navigation Strategy for Autonomous Underwater Vehicles: An Unscented Kalman Filter Based Approach

Developing reliable navigation strategies is mandatory in the field of Underwater Robotics and in particular for Autonomous Underwater Vehicles (AUVs) to ensure the correct achievement of a mission. Underwater navigation is still nowadays critical, e.g. due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS): an AUV typically proceeds for long time intervals only relying on the measurements of its on-board sensors, without any communication with the outside environment. In this context, the filtering algorithm for the estimation of the AUV state is a key factor for the performance of the system; i.e. the filtering algorithm used to estimate the state of the AUV has to guarantee a satisfactory underwater navigation accuracy. In this paper, the authors present an underwater navigation system which exploits measurements from an Inertial Measurement Unit (IMU), Doppler Velocity Log (DVL) and a Pressure Sensor (PS) for the depth, and relies on either an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) for state estimation. A comparison between the EKF approach, classically adopted in the field of underwater robotics and the UKF is given. These navigation algorithms have been experimentally validated through the data related to some sea tests with the Typhoon class AUVs, designed and assembled by the Department of Industrial Engineering of the Florence University (DIEF) for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS and European ARROWS projects. The comparison results are significant as the two filtering strategies are based on the same process and sensors models. At this initial stage of the research activity, the navigation algorithms have been tested offline. The presented results rely on the experimental navigation data acquired during two different sea missions: in the first one, Typhoon AUV #1 navigated in a Remotely Operated Vehicle (ROV) mode near Livorno, Italy, during the final demo of THESAURUS project (held in August 2013); in the latter Typhoon AUV #2 autonomously navigated near La Spezia in the framework of the NATO CommsNet13 experiment, Italy (held in September 2013). The achieved results demonstrate the effectiveness of both navigation algorithms and the superiority of the UKF without increasing the computational load. The algorithms are both affordable for online on-board AUV implementation and new tests at sea are planned for spring 2015.

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An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles

Mechatronics ◽

10.1016/j.mechatronics.2016.05.007 ◽

2016 ◽

Vol 39 ◽

pp. 185-195 ◽

Cited By ~ 41

Author(s):

B. Allotta ◽

A. Caiti ◽

L. Chisci ◽

R. Costanzi ◽

F. Di Corato ◽

...

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Navigation Algorithm

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End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

Journal of Sensors ◽

10.1155/2021/5550850 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mingrui Luo ◽

En Li ◽

Rui Guo ◽

Jiaxin Liu ◽

Zize Liang

Keyword(s):

Kalman Filter ◽

Pose Estimation ◽

Unscented Kalman Filter ◽

Rbf Neural Network ◽

Estimation Error ◽

Estimation Method ◽

Original Data ◽

Measurement Unit ◽

Complex Environments ◽

End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

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Aircraft Inertial Measurement Unit Fault Diagnosis Based on Optimal Two-Stage UKF

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20183650933 ◽

2018 ◽

Vol 36 (5) ◽

pp. 933-941

Author(s):

Qizhi He ◽

Weiguo Zhang ◽

Degang Huang ◽

Huakun Chen ◽

Jinglong Liu

Keyword(s):

Kalman Filter ◽

Fault Diagnosis ◽

Nonlinear System ◽

Inertial Measurement Unit ◽

Unscented Kalman Filter ◽

Optimal Estimation ◽

Measurement Unit ◽

Two Stage ◽

Inertial Measurement ◽

System States

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

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Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203840806 ◽

2020 ◽

Vol 38 (4) ◽

pp. 806-813

Author(s):

Qizhi He ◽

Weiguo Zhang ◽

Xiaoxiong Liu ◽

Weinan Li

Keyword(s):

Kalman Filter ◽

Random Walk ◽

Fault Diagnosis ◽

Inertial Measurement Unit ◽

Unscented Kalman Filter ◽

Random Walk Model ◽

Measurement Unit ◽

Actual Situation ◽

Two Stage ◽

Inertial Measurement

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

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Safe Navigation Algorithm for Autonomous Underwater Vehicles

Giroskopiya i Navigatsiya ◽

10.17285/0869-7035.0058 ◽

2021 ◽

Vol 29 (1) ◽

pp. 97-110

Author(s):

V.S. Bykova ◽

◽

A.I. Mashoshin ◽

I.V. Pashkevich ◽

◽

...

Keyword(s):

Control System ◽

Autonomous Underwater Vehicle ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicle ◽

Underwater Vehicles ◽

Limited Size ◽

Safe Navigation ◽

Navigation Algorithm ◽

Navigation Algorithms ◽

Ice Edge

Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.

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A Portable Support Attitude Sensing System for Accurate Attitude Estimation of Hydraulic Support Based on Unscented Kalman Filter

Sensors ◽

10.3390/s20195459 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5459 ◽

Cited By ~ 1

Author(s):

Xuliang Lu ◽

Zhongbin Wang ◽

Chao Tan ◽

Haifeng Yan ◽

Lei Si ◽

...

Keyword(s):

Kalman Filter ◽

Measurement Error ◽

Unscented Kalman Filter ◽

Attitude Estimation ◽

Measurement Unit ◽

Hydraulic Support ◽

Sensing System ◽

Rock Structure ◽

Gradient Descent Algorithm ◽

Yaw Angle

To measure the support attitude of hydraulic support, a support attitude sensing system composed of an inertial measurement unit with microelectromechanical system (MEMS) was designed in this study. Yaw angle estimation with magnetometers is disturbed by the perturbed magnetic field generated by coal rock structure and high-power equipment of shearer in automatic coal mining working face. Roll and pitch angles are estimated using the MEMS gyroscope and accelerometer, and the accuracy is not reliable with time. In order to eliminate the measurement error of the sensors and obtain the high-accuracy attitude estimation of the system, an unscented Kalman filter based on quaternion according to the characteristics of complementation of the magnetometer, accelerometer and gyroscope is applied to optimize the solution of sensor data. Then the gradient descent algorithm is used to optimize the key parameter of unscented Kalman filter, namely process noise covariance, to improve the accuracy of attitude calculation. Finally, an experiment and industrial application show that the average measurement error of yaw angle is less than 2° and that of pitch angle and roll angle is less than 1°, which proves the efficiency and feasibility of the proposed system and method.

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