Transversal navigation algorithm for Unmanned Underwater Vehicles in the polar region

Researchers are currently conducting several studies in the field of navigation systems and sensors. Even in the past, there was a lot of research regarding the field of velocity sensors for unmanned underwater vehicles (UUVs). UUVs have various services and significance in the military, scientific research, and many commercial applications due to their autonomy mechanism. So, it’s very crucial for the proper maintenance of the navigation system. Reliable navigation of unmanned underwater vehicles depends on the quality of their state determination. There are so many navigation systems available, like position determination, depth information, etc. Among them, velocity determination is now one of the most important navigational criteria for UUVs. The key source of navigational aids for different deep-sea research projects is water currents. These days, many different sensors are available to monitor the UUV’s velocity. In recent times, there have been five primary types of sensors utilized for UUV velocity forecasts. These include Doppler Velocity Logger sensors, paddlewheel sensors, optical sensors, electromagnetic sensors, and ultrasonic sensors. The most popular sensing sensor for estimating velocity at the moment is the Doppler Velocity Logger (DVL) sensor. DVL sensor is the most fully developed sensor for UUVs in recent years. In this work, we offer an overview of the field of navigation systems and sensors (especially velocity) developed for UUVs with respect to their use with tidal current sensing in the UUV setting, including their history, evolution, current research initiatives, and anticipated future.

Unmanned underwater vehicles perform inspection and maintenance tasks in complex and changing environments. Some of these tasks require synchronous navigation of multiple vehicles, which is challenging. This paper proposes a synchronous navigation scheme for two BlueROV2 underwater vehicles for a coordinated multi-vehicle task. In the proposed scheme, the vehicles perform the collaborative task of grasping, transporting, and releasing an object. In this scheme, no vehicle-to-vehicle communication is required. A model-free second-order sliding mode controller with finite-time convergence is used to accomplish this task. The controller's convergence time is user-defined and does not depend on the physical or hydrodynamic parameters of the vehicle, unlike the other finite-time controllers found in the literature. Simulation experiments were conducted to verify the controller's performance, including high ocean currents as external disturbances. Comparisons were made with two state-of-the-art controllers with finite-time convergence. The results showed that the proposed controller achieved the best results, as the user-defined convergence time was achieved for both vehicles and the collaborative task was completed, no ripples, deviations, or oscillations were observed, and no chattering occurred. The results proved the robustness of the controller in the presence of high ocean currents without the need to readjust the parameters.

This thesis presents the development and implementation of a Regional Ocean Modelling System (ROMS)-based Unmanned Underwater Vehicle (UUV) mission simulator. The primary focus is on creating a sophisticated simulation tool that integrates ROMS data to replicate realistic ocean environments and accurately mimic the behavior and data collection processes of UUVs, with a specific emphasis on the T-REMUS vehicle. The developed simulator aims to provide a safe and efficient way to develop and test advanced navigation systems for UUVs. The study involves transforming ROMS data for uniform spatial representation and simplified interpolation. Path generation algorithms were developed for missions with predetermined waypoints, featuring straight and yo-yo maneuvers. Data collection focuses on the Acoustic Doppler Current Profiler (ADCP) and Conductivity, Temperature, and Depth (CTD) sensors. Enhancements include sensor frequency adjustments and noise addition. Implemented as a Python package as well as a Graphical User Interface (GUI), this research provides a robust tool for advancing UUV navigation and data collection strategies. The simulator’s applications span mission planning, virtual ocean studies, and educational purposes, contributing significantly to oceanography and UUV navigation systems.

In recent years, floating debris in the sea has damaged the environment and harmed wildlife. Systems for collecting floating marine debris in the open ocean by installing a huge net or using a vacuum device have been proposed. However, to the best of our knowledge, there is no system for collecting marine debris in complex topographies, such as ports wherein marine debris tends to accumulate, and large vessels cannot enter. This study proposes a system to locate and collect floating marine debris by jointly using an unmanned aerial vehicle (UAV) and an unmanned underwater vehicle (UUV). The UAV discovers marine debris that cannot be observed by the UUV owing to its limited view, and the UUV is used for collection. However, for application of such a system, problems related to target detection, UUV navigation, and target collection must be solved. In the proposed system, the following components are developed: a graphical user interface to select the collection target, controller for the UUV to navigate to the target, and a collecting motion generator. We tested and evaluated the proposed cooperative system using a dynamic simulator and physical robots. As a result, we confirm that our system can achieve efficient debris collection in complex topographies, is easy to use, will reduced workload, and can be applied practically.

Mechanical Scanning Sonars (MSS) are popular underwater sensors for Unmanned Underwater Vehicles (UUV) due to their low cost, small size, and low power consumption. But due to their simplicity, there are also many research challenges related to their usage. Unfortunately, there is also a lack of data with ground truth UUV localization. We provide MSS datasets using a UUV with standard navigation sensors, i.e., an Inertial Measurement Unit (IMU) and a Doppler Velocity Log (DVL). The UUV is globally localized with a high precision optical tracking system in a large research pool to provide ground truth. The data is of interest for multiple research areas related to MSS, e.g., extraction of range information, registration of sonar scans, and especially mapping including Simultaneous Localization and Mapping (SLAM). Different parameter settings and environment conditions are covered, e.g., dynamics in the scene. The IMU and DVL data is also of interest for research on navigation independent of the MSS data. Results from navigation and mapping with an Extended Kalman Filter (EKF) are in addition provided as baseline solutions.

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

This work presents an improvement of an inertial-Doppler underwater navigation system for unmanned underwater vehicles (UUV) using a complementary range sonar. The inertial-Doppler navigation system is generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL) accompanying a magnetic compass and a depth sensor. Although conventional navigation systems update bias errors of the inertial sensors, scale effects of the DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model with additional range sonar to improve the performance of the IMU-DVL navigation system for underwater vehicles. The proposed navigation model based on inertial navigation systems includes the scale effects, the bias errors of the DVL, and the error model of the range sonar, where the order of the system states is 21. An extended Kalman filter was adopted to propagate the error covariance, updated the measurement errors and correct the state equation when the external measurements are available. Simulation conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode illustrates the effectiveness of the IMU-DVL navigation system assisted by the additional range measurement.

This paper reports a preliminary study for use of a fully dynamic vehicle process model in combined underwater communication and navigation (cooperative navigation) of underwater vehicles equipped with an acoustic modem, attitude, and depth sensors, but lacking a Doppler velocity log (DVL), and a surface vehicle equipped with an acoustic modem and GPS. We report both simulation and at-sea experimental trials with the JHU Iver3 autonomous underwater vehicle (AUV). The case of underwater vehicle navigation without a DVL is of interest in several use-cases including (a) small and low-cost underwater vehicles for which DVLs may be impractical or infeasible due to their size and cost and (b) for missions in which the vehicle's altitude above the sea floor (or depth beneath overhead ice) exceeds the DVL acoustic bottom-lock range. To the best of our knowledge, all previous studies on cooperative navigation have reported use of a kinematic process model, which works well in the presence of frequent, high-accuracy velocity measurements, as is the case when the vehicle is equipped with a DVL. This preliminary study suggests that the dynamical process model may offer a significant advantage over the purely kinematic model in the absence of frequent, high-accuracy velocity measurements, as is the case when the submerged vehicle is not equipped with a DVL.

In order to improve the positioning accuracy of two leader autonomous underwater vehicles (AUVs) in underwater cooperative navigation formation, a decentralized cooperative localization algorithm based on adaptive cubature Kalman filter (ACKF-DCL) for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation system is proposed. Firstly, the kinematic equations of the SINS are used to establish the direct filtering approach model for integrated navigation. Secondly, the Sage-Husa adaptive filtering algorithm and the sequential Kalman filtering (SKF) method are introduced into the nonlinear cubature Kalman filter (CKF) to adjust adaptively the measurement noise and maintain the positivity of the covariance matrix. Finally, the cross-correlation covariance between two AUVs is derived according to the characteristics of a decentralized data structure, so as to track the inter-AUV dependencies. And the acoustic ranging measurement is introduced to achieve relative measurement update. The lake trial demonstrates the proposed algorithm’s effectiveness and superiority, and the results show that the algorithm can effectively track the measurement noise covariance matrix and improve the positioning accuracy of both AUVs by position correction.

The Doppler inertial acoustic system (DIAS) is a compact, low-power, inertially-aided Doppler velocity navigation system which was developed to enhance the navigation capabilities of an unmanned underwater vehicle (UUV) in search, reacquisition, and inspection applications. Based on experience gained in the employment of long-baseline acoustic navigation approach using the divers acoustic navigation system (DANS) in support of the explosive ordnance disposal remote work packages program (EODRWP), DIAS provides for navigation of small subsea vehicles operating in shallow water without requiring reliance on external reference points reducing operational complexity. The primary navigation components in DIAS include a recently developed high-frequency (1200 kHz) Doppler velocity log and a relatively low-cost, low-power, miniature solid-state inertial package. Sensor data collection, prefiltering and integration is accomplished using a low-power digital signal processor (DSP) based single board computer (SBC) resulting in a system that requires less than 14 Watts power dissipation. Data is integrated using Kalman filtering techniques reducing long term errors introduced by the Doppler and short-term errors introduced by the inertial sensors. The paper describes DIAS development and testing and contrasts the accuracies of the developed system versus the previously employed long-baseline approach. The integration of DIAS within the EODRWP and at-sea employment using the Cetus AUV completes the paper.

An improved method of gyrocompass alignment for strap‐down inertial navigation system (SINS) on moving base assisted with Doppler velocity log (DVL) is proposed in this paper. After analyzing the classical gyrocompass alignment principle on static base, implementation of compass alignment on moving base is given in detail. Furthermore, based on analysis of velocity error, latitude error, and acceleration error on moving base, two improvements are introduced to ensure alignment accuracy and speed: (1) the system parameters are redesigned to decrease the acceleration interference and (2) a data repeated calculation algorithm is used in order to shorten the prolonged alignment time caused by changes in parameters. Simulation and test results indicate that the improved method can realize the alignment on moving base quickly and effectively.

The traditional strapdown inertial navigation system usually uses the gyroscope survey carrier's angular speed and the accelerator surveyed the ratio strength signal which carries on the inertial navigation. The gyroscope free strapdown inertial navigation system replaces the gyroscope with the accelerator which signal outputs compared to resolve carrier angular speed. As a result, inertia component only has accelerator which has the very high request to accelerator's precision and the aspect of elimination noise. Based on the fast Fourier transformation and the wavelet transformation, carries on de-noising processing to the accelerator belt chirp signal. The belt chirp signal was carried on with the conventional based on the Fourier transformation, carries on the analysis to the power spectrum, and to de-noise the signal. According to the wavelet transformation, the methods of wavelet threshold and multi-resolution analysis are used respective characteristic to de-noise the signal, and compares with the traditional de-noising method, obtains the result that the methods of wavelet transformation for de-noising has a better aspect on smoothness and similarity.

A Doppler velocity log (DVL) provides a three-dimensional representation of the velocity of an underwater vehicle to the vehicle’s DVL-aided inertial navigation system (INS). It is a key element in determining the precision of the INS. This paper presents an algorithm for reducing the error in the DVL as a way of improving the navigation of an autonomous underwater vehicle (AUV). We also investigate and analyze the effect that the seafloor’s deviations have on DVL measurement error. Finally, the performance of the proposed algorithm is examined through both AUV simulation and experimentally. The results show that the proposed technique can indeed effectively correct DVL errors.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.