An ultrasonic transducer array for velocity measurement in underwater vehicles

Boltryk, Peter; Hill, Martyn; Keary, A.C.; Phillips, Betty J.; Robinson, Harold C.; White, P.R.

doi:10.1016/j.ultras.2003.12.036

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…With rapid development of intelligent ROVs, various types of sensors have been used to percept the nearby surrounding environment, such as sonar and camera (Boltryk et al 2004, Chen et al 2015, Lund-Hansen et al 2018, Trslic et al 2020, Zhang et al 2022. The ultrasonic sensor is a good rangefinder in variant environment with the robustness to light intensity, magnetic field and object color, which is cost-effective and suitable for nearby target recognition (Agarwal et al 2009, Bengler et al 2014, Li et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Target recognition using rotating ultrasonic sensor for an amphibious ROV

Yang

Liu

et al. 2023

Eng. Res. Express

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Recognition and tracking of nearby obstacle targets in the inshore environment are necessary for the driving of amphibious remotely operated vehicles (ROV). In this study, the underwater target recognition and tracking in front of the ROV was conducted with a cost-effective waterproof rotating ultrasonic sensing system. An empirical detection model was developed to establish the detection characteristics of the ultrasonic sensor in the turbid underwater environment. The feature extraction of the underwater targets was performed with the density-based spatial clustering of applications with noise. The maximum relative error of targets geometric feature recognition is 3.85%. The target tracking algorithm was designed based on the Kalman filter. The rotating ultrasonic sensing system was further applied and validated on a full size amphibious tracked ROV in the turbid underwater environment. The experimental results show that the maximum recognition error during the straight movement is less than 5% after Kalman filter processing. The validity of the DBSCAN and Kalman filtering algorithms is verified during the driving of the amphibious ROV.

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Section: Introductionmentioning

confidence: 99%

Target recognition using rotating ultrasonic sensor for an amphibious ROV

Yang

Liu

et al. 2023

Eng. Res. Express

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“…It is an ultrasonic navigation aid for marine applications, in which velocity is estimated using an acoustic transmitter and a receiver array. CVLs offer advantages over Doppler velocity logs in many autonomous underwater vehicle applications since they can achieve high accuracy at low velocities even during hover maneuvers [7]. The exotic and bio-inspired way of incoming flow sensing is the artificial lateral line with pressure sensors [8].…”

Section: Introductionmentioning

confidence: 99%

On Propulsion-based Estimation of Incoming Flow Velocity

Костенко

Tolstonogov

2022

IOP Conf. Ser.: Earth Environ. Sci.

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AUV’s actuators systems are highly affected by external conditions such as incoming flow velocity. The paper aims to estimation of incoming flow velocity based on quasi-steady model of thruster. The work motivation is compensation of incoming flow effect on AUV’s actuators dynamic model for improving actuator control or a velocity estimation in case of velocity sensors absence. The equation for incoming flow velocity estimation is presented. It accounts orientation of thrusters relative to the longitudinal vehicle axis. Thruster parameters identification procedure based on bollard pull tests and marine trials are presented. Comparative results of propulsion-based velocity estimation with DVL data are discussed. Velocity estimation error between methods is less than 0.1 m/s in steady motion and less than 0.2 m/s in transition process.

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“…A DVL uses a bottom-referenced acoustic navigation method that consists in transmitting narrow beams from three or four sensors in the direction of the seafloor at different angles and the platform velocity is calculated from the measured Doppler effect. To produce the narrow beams, high frequencies are required and therefore the acoustic signals are highly attenuated, limiting its usage to shallow waters [15]. A correlation velocity log (CVL) is less constrained in this respect [16].…”

Section: Introductionmentioning

confidence: 99%

Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

et al. 2022

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Autonomous underwater vehicles require accurate navigation. Techniques such as image registration using consecutive acoustic images from a sonar have shown promising results for this task. The implementation of such techniques using sonar images augmented with deep learning (DL) networks demonstrate high navigation accuracy; this is possible even with highly compressed images. The sonar images are estimates of sampled in time (with a ping period) magnitudes of channel impulse responses representing the underwater acoustic environment. More information about the environment is contained in (almost) continuous in time estimates of the channel impulse responses. Such estimates can be obtained using full-duplex technology. Rather than using sonar images, this paper investigates the use of channel impulse response estimates for underwater platform motion estimation. The proposed system uses a single projector and a small number of receiving transducers installed on the moving platform. A DL network is used to estimate the motion in two degrees of freedom (forward/backward and sideways), using two or more consecutive impulse response estimates as the input. To train the DL network, a specially designed simulator is used to model the underwater acoustic environment, populated with multiple objects spread on the seafloor. The proposed technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher accuracy of motion estimation, compared with techniques based on the processing of sonar images, e.g., the error achieved with the technique proposed in this paper is 1.7% of the maximum platform displacement, compared to 4% achieved with a technique using sonar images. The navigation accuracy is further illustrated by examples of estimation of complex trajectories.

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An ultrasonic transducer array for velocity measurement in underwater vehicles

Cited by 16 publications

References 5 publications

Target recognition using rotating ultrasonic sensor for an amphibious ROV

Target recognition using rotating ultrasonic sensor for an amphibious ROV

On Propulsion-based Estimation of Incoming Flow Velocity

Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

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