scholarly journals Using Autonomous Underwater Vehicles for Diver Tracking and Navigation Aiding

2020 ◽  
Vol 8 (6) ◽  
pp. 413
Author(s):  
Đula Nađ ◽  
Filip Mandić ◽  
Nikola Mišković
Keyword(s):  
Life Support ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Technical Equipment ◽  
Short Baseline ◽  
Acoustic Localization ◽  
Multiple Viewpoints ◽  
Point Of Interest ◽  
Underwater Navigation ◽  
The Eu

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed exploration, monitoring, and mapping in various applications, they still lack the higher cognitive capabilities offered by a human diver. The research presented in this paper was carried out under the EU FP7 “CADDY—Cognitive Autonomous Diving Buddy”. It aims to take advantage of both human diver and AUV complementary traits by making their synergy a potential solution for mitigation of state of the art diving challenges. The AUV increases diver safety by constantly observing the diver, provides navigation aiding by directing the diver and offers assistance (e.g., lights, tool fetching, etc.). The control algorithms proposed in the paper provide a foundation for implementing these services. These algorithms use measurements from stereo-camera, sonar and ultra-short baseline acoustic localization to ensure the vehicle constantly follows and observes the diver. Additionally, the vehicle maintains a relative formation with the diver to allow observation from multiple viewpoints and to aid underwater navigation by pointing towards the next point of interest. Performance of the proposed algorithms is evaluated using results from pool experiments.

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

2015 ◽  
Author(s):  
Benedetto Allotta ◽  
Riccardo Costanzi ◽  
Enrico Meli ◽  
Alessandro Ridolfi ◽  
Luigi Chisci ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Underwater Robotics ◽  
Filtering Algorithm ◽  
Navigation Algorithms ◽  
Underwater Navigation

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.

scholarly journals Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

2021 ◽  
Vol 11 (17) ◽  
pp. 8038
Author(s):  
Dongzhou Zhan ◽  
Huarong Zheng ◽  
Wen Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Acoustic Signals ◽  
Underwater Vehicles ◽  
Ocean Currents ◽  
Acoustic Localization ◽  
And Control ◽  
Constraints Model

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

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.

Underwater Terrain Positioning Method Using Maximum a Posteriori Estimation and PCNN Model

2019 ◽  
Vol 72 (5) ◽  
pp. 1233-1253 ◽  
Author(s):  
Pengyun Chen ◽  
Pengfei Zhang ◽  
Teng Ma ◽  
Peng Shen ◽  
Ye Li ◽  
...  
Keyword(s):  
Inertial Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
A Posteriori ◽  
Positioning Accuracy ◽  
Acoustic Arrays ◽  
Positioning Method ◽  
Anchor Points ◽  
Underwater Navigation ◽  
Posteriori Estimation

Conventional underwater navigation and positioning methods for Autonomous Underwater Vehicles (AUVs) either require the installation of acoustic arrays, which make AUVs less independent, or result in cumulative errors. This paper proposes an Underwater Terrain Positioning Method (UTPM) using Maximum a Posteriori (MAP) estimation and a Pulse Coupled Neural Network (PCNN) model for highly accurate navigation by AUVs. The PCNN model is used as a secondary discriminant to effectively identify pseudo-anchor points in flat terrain feature areas and to find the true positioning point, which significantly improves the matching positioning accuracy in these areas. Simulation results show that the proposed method effectively corrects Inertial Navigation System (INS) cumulative errors and has high matching positioning accuracy, which satisfy the requirements of AUV underwater navigation and positioning.

Field Testing of Moving Short-baseline Navigation for Autonomous Underwater Vehicles using Synchronized Acoustic Messaging

2013 ◽  
Vol 30 (4) ◽  
pp. 519-535 ◽  
Author(s):  
Eric Wolbrecht ◽  
Michael Anderson ◽  
John Canning ◽  
Dean Edwards ◽  
Jim Frenzel ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Field Testing ◽  
Underwater Vehicles ◽  
Short Baseline

Development of an ultra short baseline–aided buoy for underwater targets localization

2019 ◽  
Vol 233 (4) ◽  
pp. 1212-1225 ◽  
Author(s):  
Francesco Fanelli ◽  
Niccolò Monni ◽  
Nicola Palma ◽  
Alessandro Ridolfi
Keyword(s):  
Measurement Errors ◽  
Autonomous Underwater Vehicles ◽  
Dynamic Modelling ◽  
Experimental Tests ◽  
Underwater Vehicles ◽  
Short Baseline ◽  
Vehicle Localization ◽  
Positioning Systems ◽  
Fusion Methods ◽  
Underwater Target

Autonomous underwater vehicles localization and navigation are challenging due to the lack of Global Positioning System underwater: alternative techniques have then to be used in order to measure the position of the vehicle. To this aim, sensor fusion methods based on acoustic positioning systems are often exploited. This article faces the study and the improvement of the localization of an underwater target through an ultra short baseline–aided buoy built by the Mechatronics and Dynamic Modelling Laboratory of the University of Florence. Such a buoy relies on an ultra short baseline device for the localization and is aided by a proper sensor set in order to compensate variations in its pose. First, a study of the underwater localization based on the ultra short baseline technique is provided. The measurement errors entailed by the buoy motion are then analyzed and preliminarily compensated, exploiting linear least squares methods. Subsequently, filtering techniques are considered with the aim to further increase the accuracy of the ultra short baseline measurements. Due to the nonlinearities of the sensors characteristics, extended Kalman filter has been used, with different models for stationary and moving targets. The solutions proposed have been validated through experimental tests conducted with MArine Robotic Tool for Archaeology autonomous underwater vehicles built by the Mechatronics and Dynamic Modelling Laboratory. The results evidence an improved vehicle localization, suggesting interesting future developments concerning both mechanical and computational solutions.

UNDERWATER POLE INSPECTION USING TWO MICRO AUVS

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Yoong Siang Song ◽  
Mohd Rizal Arshad
Keyword(s):  
Mechanical Design ◽  
Tracking System ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Inspection System ◽  
Clockwise Direction ◽  
Inspection Work ◽  
Underwater Navigation ◽  
Prototype Design ◽  
Lead Vehicle

This article describes the strategy to use two Autonomous Underwater Vehicles (AUVs) in underwater pole inspection work. They are called vehicle A and vehicle B. Vehicle A will surround the pole in counter clockwise direction whereas vehicle B will surround the pole in clockwise direction until the two vehicles meet. Then they will dive a certain distance and continuous surrounding the pole in opposite direction. The mechanical design of both vehicle A and vehicle B are exactly the same. The only different between them is vehicle A will make use of higher capability of underwater navigation and tracking system. Therefore, vehicle A is functioning as lead vehicle. Vehicle A and vehicle B will communicate with each other periodically for control signal dissemination and positioning error.  This article also mention about the prototype design of vehicle A And vehicle B. Some preliminary result of proposed pole inspection system is also included in this article.

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