Cooperative Navigation for Multiple AUVs Based on Relative Range Measurements with a Single Leader

2014 ◽  
Vol 556-562 ◽  
pp. 3117-3123 ◽  
Author(s):  
Xing Li Huang ◽  
Li Yan Liu ◽  
Tao Tao Lv ◽  
Wen Bai Li
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Cooperative Localization ◽  
Positioning System ◽  
Navigation Problem ◽  
Range Measurements ◽  
Cooperative Navigation ◽  
Long Baseline ◽  
Novel Method ◽  
Simulation Results

his paper deals with the cooperative navigation problem of multiple autonomous underwater vehicles (AUV). A novel method which does not depend on a beacon network like in long baseline positioning system is proposed. The principle of this approach is to realize the cooperative localization of AUVs by using relative range measurements between the leader and the follower vehicles by means of an extended Kalman filter. Simulation results that validate the effectiveness of this approach are presented.

scholarly journals Cooperative Localization Method for AUV Using Acoustic Communication Measurement

2012 ◽  
Vol 4 ◽  
pp. 227-231 ◽  
Author(s):  
Li Chuan Zhang ◽  
Ming Yong Liu ◽  
Fu Bin Zhang
Keyword(s):  
Acoustic Communication ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Cooperative Localization ◽  
Relative Location ◽  
Navigation Problem ◽  
Cooperative Navigation ◽  
Acoustic Device ◽  
Navigation Accuracy

In this paper, we propose an algorithm based on double acoustic measurement for cooperative navigation of multiple autonomous underwater vehicles. Research on cooperative navigation of AUV is an important topic to solve the navigation problem in long range and deep sea. We investigate the improvement in navigation accuracy. In the Leader-follow structure, the leader AUV is equipped with high precision navigation system, and the follow AUV is equipped with low precision navigation system. They all are equipped with acoustic device to measure relative location. Traditionally geometry triangulation method is used to calculate the position of follow AUV, the method may cause fault error solution. Double acoustic communication measurement method was designed, which fused the proprioceptive and exteroceptive sensors. The research results prove that the navigation accuracy has been improved effectively.

Observability Analysis for MAUVs Cooperative Navigation System Based on Moving Long Baseline

2013 ◽  
Vol 475-476 ◽  
pp. 609-615
Author(s):  
Peng Ma ◽  
Fu Bin Zhang ◽  
De Min Xu ◽  
Shao Kun Yang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Simulation Experiments ◽  
Cooperative Navigation ◽  
Long Baseline ◽  
Simulation Results ◽  
Moving Long Baseline ◽  
Theoretical Results

This paper addresses the observability problem of 2D Multiple Autonomous Underwater Vehicles (MAUVs) cooperative navigation system. We derive the conditions to keep the local weak observability of navigation system using the Lie derivatives, and characterize the unobservable trajectories of AUVs. We design a series of simulation experiments using the Extended Kalman Filter (EKF) to verify the theoretical results. Finally, the simulation results show that the good performance of navigation system can be presented if avoiding the unobservable trajectories of AUVs.

scholarly journals A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xinnan Fan ◽  
Zhongjian Wu ◽  
Jianjun Ni ◽  
Chengming Luo
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Simulation Experiments ◽  
Range Measurements ◽  
Acoustic Localization ◽  
Long Baseline ◽  
Key Factor

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

scholarly journals Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Ran Wang ◽  
Xin Wang ◽  
MingMing Zhu ◽  
YinFu Lin
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Visual Localization ◽  
Visualization Method ◽  
Localization And Mapping ◽  
Real Time Visualization ◽  
Novel Method ◽  
Vision Based Localization ◽  
Slam Algorithm ◽  
Localization Systems

Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.

scholarly journals Distributed synchronization of autonomous underwater vehicles with memorized protocol

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141984414 ◽  
Author(s):  
Chao Ma ◽  
Wei Wu
Keyword(s):  
Model Transformation ◽  
Autonomous Underwater Vehicles ◽  
Theoretical Method ◽  
Underwater Vehicles ◽  
Functional Method ◽  
Synchronization Problem ◽  
Distributed Synchronization ◽  
Simulation Results ◽  
Synchronization Performance ◽  
Synchronization Protocol

This article investigates the distributed synchronization problem of autonomous underwater vehicles by developing a novel synchronization protocol with memorized controller. More precisely, the memory information for information exchanges of autonomous underwater vehicles is utilized such that the synchronization performance can be improved. By employing the Lyapunov–Krasovskii functional method with model transformation, sufficient criteria are established for guaranteeing the synchronization, and the corresponding distributed synchronization controllers are designed based on matrix techniques. Finally, the effectiveness and benefits of our theoretical method are supported by an illustrative example with simulation results.

Research on Signal Aliasing in Long Baseline Positioning System

2014 ◽  
Vol 981 ◽  
pp. 435-439
Author(s):  
Jun Cao ◽  
Da Jun Sun ◽  
Dian Lun Zhang
Keyword(s):  
Signal Detection ◽  
Underwater Vehicle ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Long Baseline ◽  
Simulation Results ◽  
Signal Overlap

In the long baseline positioning system, underwater vehicle use transponder reply signals for positioning and navigation, reply signals aliasing is the main reason of inaccurate detection and instability detection. This paper aimed at the problem and research on signal aliasing in long baseline positioning system, theoretical and simulation results show that: Both overlap degree and overlap energy have a certain distribution. The size of signal aliasing area associated with signal overlap degree. The number of signal aliasing is different in different aliasing area. Signal aliasing will affect signal detection, and reduce the long baseline positioning accuracy.

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.

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