scholarly journals Adaptive Navigation Algorithm with Deep Learning for Autonomous Underwater Vehicle

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6406
Author(s):  
Hui Ma ◽  
Xiaokai Mu ◽  
Bo He
Keyword(s):  
Deep Learning ◽  
Microelectromechanical Systems ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Low Frequency ◽  
Doppler Velocity ◽  
Position Information ◽  
Localization Accuracy ◽  
Navigation Algorithm ◽  
Frequency Position

Precise navigation is essential for autonomous underwater vehicles (AUVs). The measurement deviation of the navigation sensors, especially the microelectromechanical systems (MEMS) sensors, is a crucial factor that affects the localization accuracy. Deep learning is a novel method to solve this problem. However, the calculation cycle and robustness of the deep learning method may be insufficient in practical application. This paper proposes an adaptive navigation algorithm with deep learning to address these questions and realize accurate navigation. Firstly, this algorithm uses deep learning to generate low-frequency position information to correct the error accumulation of the navigation system. Secondly, the χ2 rule is selected to judge if the Doppler velocity log (DVL) measurement fails, which could avoid interference from DVL outliers. Thirdly, the adaptive filter, based on the variational Bayesian (VB) method, is employed to estimate the navigation information simultaneous with the measurement covariance, improving navigation accuracy even more. The experimental results, based on AUV field data, show that the proposed algorithm could realize robust navigation performance and significantly improve position accuracy.

scholarly journals An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2468
Author(s):  
Ri Lin ◽  
Feng Zhang ◽  
Dejun Li ◽  
Mingwei Lin ◽  
Gengli Zhou ◽  
...  
Keyword(s):  
Data Exchange ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Optimization Method ◽  
Short Baseline ◽  
Localization Accuracy ◽  
Localization Method ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Update Rate

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.

Safe Navigation Algorithm for Autonomous Underwater Vehicles

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.

SIMULTANEOUS COOPERATIVE LOCALIZATION FOR AUVs USING RANGE-ONLY SENSORS

2011 ◽  
Vol 08 (02) ◽  
pp. 117-132 ◽  
Author(s):  
ALI JABAR RASHIDI ◽  
SAEED MOHAMMADLOO
Keyword(s):  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Measurement Unit ◽  
Cooperative Localization ◽  
Position Information ◽  
Localization Accuracy ◽  
Long Baseline ◽  
Localization And Mapping ◽  
Filter Process ◽  
Sonar Sensor

The absence of GPS underwater makes navigation for autonomous underwater vehicles (AUVs) a challenge. Moreover, the use of static beacons in the form of a long baseline (LBL) array limits the operation area to a few square kilometers and requires substantial deployment effort before operations. In this paper, an algorithm for cooperative localization of AUVs is proposed. We describe a form of cooperative Simultaneous Localization and Mapping (SLAM). Each of the robots in the group is equipped with an Inertial Measurement Unit (IMU) and some of them have a range-only sonar sensor that can determine the relative distance to the others. Two estimators, in the form of a Kalman filter, process the available position information from all the members of the team and produce a pose estimate for every one of them. Simulation results are presented for a typical localization example of three AUVs formation in a large environment and indirect trajectory. The results show that our proposed method offers good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, which validates that it is an economical and practical localization approach.

scholarly journals A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4576
Author(s):  
Xiaozhen Yan ◽  
Yipeng Yang ◽  
Qinghua Luo ◽  
Yunsai Chen ◽  
Cong Hu
Keyword(s):  
Adaptive Filtering ◽  
Autonomous Underwater Vehicles ◽  
Doppler Velocity ◽  
Positioning System ◽  
Positioning Error ◽  
Error Statistics ◽  
Positioning Accuracy ◽  
Position Information ◽  
Filtering Method ◽  
Gain Compensation

Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy.

scholarly journals NAVIGATION AND MOTION CONTROL SYSTEMS OF THE AUTONOMOUS UNDERWATER VEHICLE

2020 ◽  
Vol 4 ◽  
pp. 38-50
Author(s):  
Dmitry Antonov ◽  
Leonid Kolganov ◽  
Aleksey Savkin ◽  
Egor Chekhov ◽  
Maxim Ryabinkin
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Control Systems ◽  
Motion Control ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Transportation Safety ◽  
Doppler Velocity ◽  
Using Data

Autonomous underwater vehicles (AUVs) are widely used and have proven their effectiveness in tasks such as transportation safety, area monitoring and seafloor mapping. When developing AUV’s navigation and control systems, the engineers have to ensure the required levels of accuracy and reliability for solving navigation and motion control tasks in autonomous underwater operation under restrictions on the overall dimensions and power consumption of the AUV. The main purpose of this paper is to present preliminary results of AUV navigation and motion control systems development. The AUV’s navigation system is built around strapdown inertial navigation system (SINS) designed specifically for this AUV. When surfaced, position and angular SINS correction is performed using data from dual-antenna GNSS receiver and doppler velocity log (DVL). When underwater, SINS position and velocity correction is performed using acoustic navigation system (ANS) and DVL data. AUV’s control system provides manual and automatic control. Manual control is carried out in real-time by operator via fiber-optic cable using a joystick. Automatic control allows AUV to move independently along a specified trajectory at a given depth and speed. The AUV also has a collision avoidance system that utilizes readings from a forward-facing acoustic rangefinder to estimate time before impact based on AUV’s analytic model. If possible collision is detected, information is transmitted to the control system so that a further appropriate action can be taken. Computer simulation utilizing the analytic AUV model was used in order to check the performance characteristics of the designed control and navigation algorithms. After confirming the operability of the developed algorithms, preliminary tests of the AUV were carried out. During the tests, AUV’s on-board equipment and navigation system readings were recorded and compared to the readings of the reference system, which was also installed on the AUV. During the tests, the dynamic characteristics of the AUV were evaluated. AUV’s characteristics obtained during simulation and testing will be used as a reference during future development

scholarly journals Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

2018 ◽  
Vol 25 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Tomasz Praczyk
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Environmental Influence ◽  
Autonomous Underwater Vehicles ◽  
Dead Reckoning ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Doppler Velocity ◽  
Time Record ◽  
Navigational System ◽  
Vehicle Movement

Abstract In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV.

Conceptual Design of Navigation of an AUV for Monitoring CCS Site at Deep Sea Bottom

2011 ◽  
Author(s):  
Yoshitaka Watanabe ◽  
Hiroshi Yoshida ◽  
Hiroshi Ochi ◽  
Tadahiro Hyakudome ◽  
Shojiro Ishibashi ◽  
...  
Keyword(s):  
Conceptual Design ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Acoustic Method ◽  
Doppler Velocity ◽  
Depth Sensor ◽  
Short Baseline ◽  
Position Information ◽  
Sea Bottom ◽  
Acoustic Methods

We, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), are developing an autonomous underwater vehicle (AUV) whose main mission is monitoring a site at the sea bottom for the carbon dioxide capture and storage (CCS). The AUV cruises very near the sea bottom, and is equipped with chemical sensors in order to detect escape of CO2 from sub-bottom. Of course, the position information of the AUV is critical information for the monitoring. In this paper, a conceptual design of navigation of the AUV is described. Recently, navigation of AUV is implemented by integrating multiple navigation devices including inertial navigation system (INS), Doppler velocity log (DVL), depth sensor, acoustic navigation system, and others. The AUV under construction will be equipped with these navigation sensors, and will integrate those sensors’ outputs to navigate herself. In order to measure the absolute position of the AUV the acoustic method is one of fundamental technique. At the first step of development of the AUV, three acoustic methods are considered to adopt. The three methods are super short baseline (SSBL) method which is a tracking from support ship or other surface station, long baseline (LBL) which is navigation based on preplaced acoustic transponders, and virtual LBL (VLBL) which is navigation based on only single transponder. These acoustic methods are integrated with the navigation result of INS, depth sensor, and DVL. The three methods are used in each appropriate case. Which feature of observation is desired simplicity, accuracy, or independence from support ship and time efficiency? The acoustic method is influenced by environment, and also output of other sensors is depending on the environment, for example the DVL miss the data when the terrain is with many up-hills and down-hills. The integration or filtering parameters of the navigation should be adjusted depending on the influential environmental factor.

A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Robotica ◽  
2021 ◽  
pp. 1-27
Author(s):  
Taha Elmokadem ◽  
Andrey V. Savkin
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicles ◽  
Practical Implementation ◽  
Challenging Problem ◽  
Quadrotor Uav ◽  
Navigation Algorithm ◽  
Aerial Vehicles ◽  
Quadrotor Uavs

Abstract Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

scholarly journals TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 782
Author(s):  
Shuo Cao ◽  
Honglei Qin ◽  
Li Cong ◽  
Yingtao Huang
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Satellite System ◽  
Time Of Arrival ◽  
Military Operations ◽  
Position Information ◽  
Cooperative Navigation ◽  
Navigation Algorithm ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

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