A Transponder Array Survey Method Using A Combined Shortbaseline/Long-Baseline Acoustic Position Reference System

Strapdown inertial navigation system (SINS)/ ultra- short baseline (USBL) integrated navigation system is widely used in navigation and positioning of underwater vehicle. In an unknown and complex underwater environment, unstable USBL measurement information can lead to decreased navigation accuracy. In the traditional method, the interference to USBL is uniformly modeled as outliers, but in practice, it is also represented as slowly varying errors, update irregular errors. The latter two errors are not easy to be detected at the beginning of occurrence. Therefore, a SINS/USBL integrated navigation technology based on novel adaptive neural fuzzy inference system (ANFIS) is proposed. This model can detect and compensate abnormal USBL information and maximize USBL information. In order to improve the prediction accuracy of ANFIS, an ANFIS algorithm based on variational Bayesian Kalman filter and principal component analysis (PCA) is proposed to avoid the decrease of positioning accuracy caused by false compensation. The simulation shows that the designed ANFIS algorithm can accurately predict the above anomalies. And it can overcome the problem that USBL measurement anomalies are difficult to be detected at the initial stage. Combined with the proposed integrated navigation technology, it obtain the same positioning accuracy in complex environment as in good environment. The effectiveness and robustness of the proposed method are verified by river experiment.

The high precision positioning and navigation of underwater vehicles is one of the key technologies for deep-sea exploration and ocean exploration, the underwater localization systems can be categorized into two main types: Inertial Navigation System (INS) and acoustic positioning system. INS is widely used in underwater navigation on account of its autonomy, however, without the information aiding from additional sensors, the errors of inertial measurement units accumulate with time passes. The acoustic positioning system can be divided into three types: Long Base Line (LBL), Short Base Line (SBL) and Ultra Short Base Line (USBL). They are defined according to the distance between transponders. The short baseline (SBL) positioning system includes more than 3 transponders to form an acoustic array, and the formation of the array is normally triangle or quadrilateral. The distance between the transponder is accurately measured and generally more than 10m. The acoustic array coordinate system is built based on the transponder and its relationship with the carrier coordinate system of the vehicle is determined by conventional method, the main disadvantage of the SBL positioning system is that the baseline length is relatively large in order to achieve high accuracy in deep-water measurement. However, as the lateral length of the vehicles is small, it is difficult to make sure the transponders be placed way from each other, meanwhile it is difficult to calibrate multiple transponders and avoid noise. To solve this problem, this paper present an inertial navigation system and single range navigation system (SRNS) based on one acoustic transponder, the navigation system consist of an acoustic transponder, a pressure transponder and inertial sensors mounted on the vehicle and an answering device transponder which is laoid out at tens of meters deep straight under the buoy. This paper studies the measurement equation's nonlinearity effect of the INS/SRNS, establishes state equation and measurement equation of the INS/SRNS integrated system. To deal with the nonlinear noise problems of transponders, meanwhile, in view that fault detection based on traditional state chi-square hypothesis testing cannot determine the specific cause of the fault, and could only determine the validity of measure information, a new fault detection algorithm based on dual-state method is adopted. The application of this method on underwater integrated navigation system demonstrates that the algorithm can rapidly and accurately detecting and identify the faults in the system. Experiments on QIANDAO Lake show that the proposed method restrains the divergence trend error of the integrated system's navigation, which verify the effectiveness of the single range integrated navigation.

This paper presents the integration of an Ultra Short Base Line (USBL) acoustic modem and positioning device in a two-parallel Extended Kalman Filter (EKF) multisensor navigation schema for an Autonomous Underwater Vehicle (AUV). The system consists of a first odometric EKF position estimator fed with the measurements provided by an Inertial Measurement Unit (IMU), a Doppler Velocity Log (DVL), a visual tracker and a pressure sensor, and a second corrective EKF that fuses the previous sensors with the Global Positioning System (GPS) and the corrected delayed-state Ultra Short Base Line (USBL) position data. The first filter is aimed to compute a highly reliable vehicle position estimation from the sensor suit that produces high frequency relative navigation data. This estimation is essential to correct the delay of the USBL position measurements. The objective of the second filter is to give the vehicle a global pose, eliminating the drift inherent to dead-reckoning sensors by integrating absolute positions (GPS and USBL) provided at low frequencies. The effects of the USBL integration in the global localization module are evaluated in a simulated environment, and compared to a ground truth trajectory with the pose estimates given by the first EKF, the USBL raw data and the output of the second EKF, showing an effective reduction of the trajectory error.

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions.

The installation error of an acoustic transceiver array is one of the important error sources in an ultra-short baseline (USBL) system. In a USBL system with a positioning accuracy of 0.5%, an installation error angle of 1° will lead to a positioning error of 1.7% times the slant distance. In this paper, a dual transponder-based installation angle error calibration method for USBL is proposed. First, the positioning errors induced by various installation angles are deduced and analysed using the linear measurement of seafloor targets. Then, an iterative algorithm is proposed that estimates the rolling alignment error, pitching alignment error, and heading alignment error, in that order. The simulation and experienced results show that, after three iterations, the estimates of the three alignment errors can converge quickly, all of the estimates converge to within 0.001° and the estimated values are very close to the true values. The horizontal positioning error caused by the installation error angle can be reduced by nearly 75%. The method has good effectiveness and robustness, and can greatly improve the positioning accuracy of the USBL system.

In this work, a low-cost low-power navigation solution for autonomous underwater vehicles is introduced utilizing a Micro-Electro-Mechanical System (MEMS) inertial sensor and an ultra short baseline (USBL) system. The complete signal processing is implemented on a cheap 16-bit fixed-point arithmetic microcontroller. For data fusion and calibration, an error state Kalman filter in square root form is used, which preserves stability in case of rounding errors. To further reduce the influence of rounding errors, a stochastic rounding scheme is applied. The USBL measurements are integrated using tightly coupled data fusion by deriving the observation functions separately for range, elevation, and azimuth angles. The effectiveness of the fixed point implementation with stochastic rounding is demonstrated on a simulation, and the the complete setup is tested in a field test. The results of the field test show an improved accuracy of the tightly coupled data fusion in comparison with loosely coupled data fusion. It is also shown that the applied rounding schemes can bring the fixed-point estimates to a near floating point accuracy.

To provide a low-cost modular easily deployable platform for advancing marine autonomy and supporting multi-vehicle missions, an Autonomous Surface Vehicles (ASV) called BREAM (Boat for Robotic Engineering and Applied Machine-Learning) is de veloped by Purdue University. BREAM is equipped with a Ultra Short Baseline (USBL) system for underwater localization, an acoustic modem for underwater communication, and a portable and adaptable docking station for underwater power/data transfer. This paper discusses the design, construction, control and experimental validation of the vehicle's autonomous capabilities.

The lack of GPS signal in the underwater environment poses limitations in terms of localization and navigation of mobile robots. Strategies based on acoustic localization systems are employed to improve underwater navigation. In this paper we describe a first step towards the development of a marine system of systems involving autonomous mobile nodes. The approach relies on communication networking between an Autonomous Surface Vehicle (ASV), equipped with an Ultra Short BaseLine (USBL) device, and an Autonomous Underwater Vehicle (AUV). An active acoustic communication protocol prioritizes the AUV positioning rate, whereas a model-based navigation filter handles the delayed measurements caused by the acoustic communication latency. The system has been tested in a real marine environment to analyze its behavior and the quality of the navigation estimation. The experimental results show that the navigation algorithm on-board the AUV provides an estimate of its position with an error of a few meters with respect to the GPS ground-truth, over a total path of approximately 210m, exploiting acoustic positioning data provided by the ASV in order to limit drift problems.

This paper presents a new ultrashort baseline (USBL) tightly coupled integration technique to enhance error estimation in low‐cost strapdown inertial navigation systems (INSs), with application to underwater vehicles. In the proposed strategy, the acoustic array spatial information is directly exploited in an extended Kalman filter (EKF) implemented in a direct feedback structure. Instead of using the USBL position fixes or computed range and elevation/bearing angles to correct the INS error drifts, as in classical loosely coupled strategies, the novel tightly coupled strategy directly embeds in the EKF the round‐trip‐time and time‐difference‐of‐arrival of the acoustic signals arriving at the onboard receivers. The enhanced performance of the proposed filtering technique is evidenced both through extensive numerical simulations and with experimental data obtained in field tests at sea. The tightly coupled filter is also shown to be able to operate closer to theoretical performance lower bounds, such as the posterior Cramér‐Rao lower bound, using Monte‐Carlo simulations. This paper details the design and description of an USBL/INS prototype to be used as a low‐cost navigation system, including the acoustic processing and positioning system, fully developed in‐house. The developed system validates the usage of the proposed technique with real data in real world operation scenarios, and its enhanced performance compared to classical strategies is evaluated experimentally (median improvement level of 15% in typical operating conditions). Improved and faster convergence to nominal trajectories from multiple initial conditions, as well as enhanced accelerometer and rate gyros estimation capabilities, are also demonstrated experimentally for the new tightly coupled filter.

In this paper we present a terrain-aided particle filter to localize a freely drifting underwater vehicle. The vehicle is a bottom imaging Lagrangian float used for habitat classification, monitoring and fish abundance. During operation the vehicle captures down looking images at a controlled altitude above the bottom. Direct navigation information is often, but not always, recorded with a ultra short baseline (USBL) acoustic system. The presented methodology provides an alternate method for georeferencing when USBL is unavailable. The implemented particle filter utilizes a background bathymetry map and visual odometry as a motion mode. The particle filter is implemented using the Robot Operating System (ROS) and Orocos Bayesian Filtering Library (BFL). The Grid Map package is used to store and retrieve the bathymetryic data. Results using data collected on field deployments in the Hawaiian islands in 2018 show the method is able to effectively utilize the terrain information and produce drift trajectories which closely match the recorded USBL data. Utilizing the method allows the float system to be deployed with minimal ship-side support while still maintaining the georeferencing critical to the end use of the collected images.

Ultra-Short Baseline (USBL) systems are susceptible to installation errors that necessitate accurate calibration before deployment. In this paper, we propose an innovative calibration method that does not require time synchronization, enhancing calibration accuracy and mitigating the effects of acoustic array motion. First, we construct a novel installation error model based on the slant range difference to estimate signal sending time. Then, we designed an improved filtering algorithm—variational Bayesian unscented Kalman filter-range (VBUKF-r). In VBUKFr, to address challenges related to unknown and time-varying measurement noise covariance matrix (MNCM), we integrate variational Bayesian methods with the unscented Kalman filter (UKF) and enhance the accuracy of MNCM estimation by analyzing the relationship between ranging noise and distance. Furthermore, we utilize matrix diagonalization instead of Cholesky decomposition to resolve computational issues associated with non-positive definite state covariance matrixs. Finally, we propose a K-means clustering iterative optimization strategy for VBUKF-r to mitigate the influence of initial measurement vectors on parameter estimation. Simulation analysis and sea trials demonstrate that our algorithm significantly outperforms existing methods.

Within the EU FP7 project “Cognitive autonomous diving buddy (CADDY)”, work has been made to assist and monitor divers through Autonomous Underwater Vehicles (AUVs) during their long underwater expeditions. To achieve this goal, one milestone is to give the AUV the capability to track the diver's whereabouts at all times. Inertial sensors are mounted on the diver's body to transmit acoustically his relative position and orientation to the AUV using an Ultra-Short Baseline (USBL) system. However, the acoustic system is prone to give erroneous data depending on the surrounding geography, modems alignment and sensors calibration, plus its low transmission rate does not allow real time performance. To overcome these drawbacks and complement the acoustic set-up, this paper presents a general framework to detect and estimate the diver's body pose based on generated point clouds from a stereo camera. Our method is based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) that captures the temporal relations between global point cloud descriptors as they change with the diver movements. Since the analysis is made on time sequences rather than on one-shot visual information, the framework is robust against the distortions and poor quality typical in underwater while still providing real time performance. We focus on the description of the image-processing and LSTM-RNN pipeline, as well on its validation with a dataset created during several field trial experiments.

This paper reports on the status and design of the operational remotely operated vehicle “Victor 6000”, rated for 6000 m depth and of the 3000 m depth autonomous underwater vehicle “Asterxx” operated by Ifremer the French Institute for Sea Exploitation. Victor 6000 is part of a global system built of subsystems, “scientific modules,” winch and cable, dead weight and umbilical, positioning subsystem using acoustic ultra short base line (USBL) techniques, and software for data and dive management. In 2005 the system was equipped with a second toolsled dedicated to high resolution mapping of the sea bed, with both acoustical and optical devices. Asterxx, the first autonomous underwater vehicle (AUV) developed by IFREMER, is 4.5 m in length with a diameter of 0.69 m. Depending on the payload, its weight is between 600 and 800 kg in air,with a diving depth of 3000 m. Its cruising speed is between 0.5 and 2.5 ms. The AUV is capable of carrying various payloads for a wide spectrum of applications. The vehicle can cruise up to 100 km. For coastal applications this vehicle is operable by a limited crew potentially from a nonspecialized vessel. Both vehicles are used for oceanography as well as to continually evolve underwater systems to address new scientific challenges. © 2007 Wiley Periodicals, Inc.

Utilization of autonomous underwater vehicle (AUV) and remotely operated vehicle (ROV) is absolutely necessary when we explore and develop submarine resources. Positioning of underwater vehicle is one of the keys to the development of submarine resources. Many researchers have been seeking the method to locate a position of underwater vehicle with high accuracy for many years. Then, we designed the positioning system integrated long base line (LBL) navigation system and super short base line (SSBL) navigation system using seafloor acoustic reference station: acoustic mirror transponder (MT)[1]. The positioning system consists of seafloor and on-board units. The seafloor unit is set of a MTs. MTs are deployed on the seafloor and serve as seafloor reference points for subsea positioning. The onboard unit, which should be mounted on the underwater vehicle, consists of a ring laser gyro (RLG) which gives the unit's attitude and the positioning system integrated LBL and SSBL, and so on. LBL system uses more than three MTs to measure distances. Meanwhile, SSBL system uses a MT to measure distance and direction. We conducted a performance test of the positioning system in Tateyama bay in November 2012. The purpose of this test is to evaluate the accuracy of the positioning based on units of LBL and SSBL of the positioning system. Three sets of the reference seafloor MT were installed on the seafloor. The positioning system was mounted on the surface platform. We had towed the surface platform along designed traverse lines. During the sea trials, the positions of the platform are estimated with ranging and direction data from the LBL and SSBL units. RLG measures the attitude of the platform. The attitude of platform is necessary to change platform's relative coordinate system to absolute coordinate system. Positions of the platform are also measured with a GPS system. We will evaluate newly developed positioning system by comparing between these two sets of the positions. Then, we developed the essential part in the system integrated LBL and SSBL.

Cooperation among heterogeneous marine vehicles can offer several benefits to underwater applications, such as supporting the navigation of submerged robots by leveraging the information available to a surface vehicle. Within such context, this paper describes an acoustic positioning and communication protocol developed for the localisation of multiple Autonomous Underwater Vehicles (AUVs) by means of an Autonomous Surface Vehicle (ASV) equipped with an Ultra-Short BaseLine (USBL) device. To avoid packet collisions, access to the acoustic channel is managed through a time slot division mechanism. Unlike a classical Time Division Multiple Access (TDMA) protocol, the approach considered in this work consists of a centralised scheduling handled by the ASV, in which AUVs are localised at regular time epoch but can transmit only when queried. This allows to prioritise the number of position measurements taken by the ASV and then relayed back to the submerged vehicles, with the final goal of improving the navigation accuracy of the latter. The solution is also designed to handle the latency of acoustic communication and to correctly associate each position measurement with the corresponding acquisition time. Furthermore, it does not require any a priori synchronisation between the clocks of the vehicles involved and ensures complete decoupling between their navigation algorithms. Experimental activities in very-shallow waters, involving an ASV and two target nodes, were carried out to validate the multi-AUVs positioning system and to provide a characterisation of its performance.