A feasibility analysis on using bathymetry for navigation of autonomous underwater vehicles

Near underwater structures or the surface of shallow water, acoustic sensing based navigation of autonomous underwater vehicles (AUVs) suffers from inaccurate positioning which is caused by acoustic sensing such as multi-pass and noisy data. In the paper a new underwater navigation method for AUVs is proposed based on artificial underwater landmarks which are recognized by a vision system implemented in the robot. An underwater image processing strategy and an underwater vision environment analysis method are introduced to improve reliability of the underwater vision system. Experiments were carried out to demonstrate the efficiency of the proposed navigation method of AUVs.

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

One-way-travel-time (OWTT) acoustic ranging has received considerable attention as improvements to acoustic modems and electronic clocks have made it a feasible navigation tool. This paper reports the results of simulations investigating the effect of utilizing inter-vehicle ranging for autonomous underwater vehicle (AUV) navigation. In these simulations, a fleet of AUVs operates in shallow water with a pair of fixed transponders. A rigid timing cycle for acoustic communications was implemented with a message queuing approach to simulate the handicaps of underwater acoustic communication. Furthermore, a simple path following algorithm was used to navigate the AUVs through a waypoint course, and a kinematic motion model was used to simulate AUV movement. The position of each vehicle in the fleet was estimated independently by combining the propagation steps of an extended Kalman filter with the update equations of an extended information filter. Results of the simulations showed the addition of inter-vehicle ranging improved accuracy by 1–2 cm when navigating using four fixed transponders and by 9–24 cm when navigating using two fixed transponders.

Precise navigation of autonomous underwater vehicles (AUV) is one of the most important challenges for marine applications. Current 2-dimensional (2D) navigation systems (algorithms) are effective to autonomous surface vehicles (ASV) operations, but still have some limitations for AUV navigation in 3D underwater environment. In this paper, a 3D range navigation algorithm is proposed that is potentially useful for small AUVs that have severe volume and power constraints. This algorithm enables an AUV to compute its trajectory in the presence of unknown currents and simultaneously estimate the currents by using range measurements from a single known location (beacon). The principal work here is the observability analysis of the proposed navigation system that characterizes observable AUV trajectories using novel approaches towards uniform observability of linear time varying systems. Then, implementation of the 3D range navigation algorithm as a discrete time extended Kalman filter is given based on the observability results and kinematic model of the system. Finally, a set of comparative simulation results verify the validity of the navigation algorithm and the system's observability related to trajectories.

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

With the increasing exploration of the marine resources, cooperative navigation (CN) of multi autonomous underwater vehicles (AUVs) can improve exploration efficiency and accuracy. In order to improve the positioning accuracy in the CN, we propose the Information Entropy (IE) algorithm, which can evaluate the measurement informations, and choosing the optimal measurement informations to update the positions of the AUVs, decreasing the influences of the low quality measurement informations to the CN. In this paper, we study the influences of choosing different AUVs' states and the amounts of the measurement informations, and we consider two scenarios, the first is that AUVs equip the equal accuracy navigation equipment; the second is that AUVs equip the different accuracy navigation equipment, which there are several AUVs have the high accuracy equipment, and the others have the low accuracy equipment. Combining with the nonlinear filtering technique, we find using the IE algorithm can reduce the positioning errors of the AUVs compared with selecting measurement informations randomly by the simulation results.

In this paper, we propose a state estimation method for navigation of multiple Autonomous Underwater Vehicles (AUVs) with limited communication traffic. Several positioning methods for a single hovering type AUV have been proposed in order to achieve different kind of missions. We had proposed a navigation method of multiple AUVs aimed at unexplored and wide seafloor surveys. In this method, the AUVs need to share their estimated states. However, typical data rates of acoustic communications in underwater environments are too small to share the states with each other constantly. In order to overcome the problem, we propose a state estimation method where the stochastic state estimator “Particle Filter” is applied to multiple AUVs. The information about the estimated states is approximated by “Particle Clustering” using a clustering method (k-means) and a model evaluation method by AIC (Akaike Information Criterion) to select the optimal size of clusters and reduce the required communication data size. Through simulations, the proposed method succeeded in stable positioning and reduction of the data size required to share.

Single-beacon navigation for group autonomous underwater vehicles (AUVs) suffers from long navigation period and asynchronization. This paper addresses the problem by introducing the single-input-multiple-output (SIMO) model, where, a single AUV transmits the signal and other AUVs receive the signal. Utilizing the time-delay of signals from the beacon and the sender, the navigation for all AUVs is achieved simultaneously, and the navigation period is dramatically decreased. Moreover, based on the new SIMO model, a tracking method utilizing extended Kalman filter is also put forward to increase the navigation precision and reduce the computational burden. Finally, a coordinate fusion algorithm based on the location, error, and time-delay is presented, which fuses the independent AUV coordinates to be a higher precision holistic coordinate system for group AUVs. Simulation results show the effectiveness of the proposed method.

An H∞ controller combined with an Artificial Potential Field Method (APFM) was applied to seabed navigation for Autonomous Underwater Vehicles (AUVs), aimed particularly at obstacle avoidance and bottom-following operations in the vertical plane. Depth control and altitude control prevented the AUV from colliding with the sea bottom or with obstacles and prevented the AUV from diving beyond its maximum depth limit when bottom following. Simulation and laboratory trials with various seabed contours indicated that with the H∞ controller, the AUV was able to safely reach appointed destinations without collisions. Tests also showed that the H∞ controller was robust and suppressed interference, hence ensuring the precision of its navigation control. The proposed H∞ controller combined with the APFM has thus been proved to be both feasible and effective.

Single beacon passive positioning is crucial for the covert and rapid navigation of autonomous underwater vehicles (AUVs). Traditional methods, which assume a fixed beacon position, known sound speed, and error-free AUV heading, limit the applicability and accuracy of passive positioning systems. In this article, we proposed an angle-based passive positioning algorithm, underwater moving bearing angle of arrival (U-MBAOA), to solve the above problems. First, we replace the bottom-anchored fixed beacon with a mobile surface beacon and develop a dynamic positioning model to accommodate its movement. Then, to enhance the positioning accuracy, we parameterized the sound speed and derived a weighted least squares method to estimate the sound speed. Finally, we constructed an error model for heading errors and positioning under a straight-line trajectory and combined this model with U-MBAOA, achieving the estimation of heading errors in passive positioning for the first time. Through simulation analysis and sea tests, we confirmed that our algorithms outperform the existing algorithms.

The primary objective of this work is to solve the problem that the long baseline (LBL) positioning system has poor real-time positioning results during the navigation for autonomous underwater vehicles (AUVs). In this paper, it is proposed to use the unscented Kalman filter (UKF) to enhance the navigation and positioning accuracy of the AUV, using the speed information measured by the Doppler Velocity Log (DVL) and the ranging information obtained by the acoustic beacons. Utilizing the unscented transformation to perform integrated navigation, it can avoid linearization truncation errors and Jacobi matrix computation compared with extended Kalman filtering (EKF). The experimental results verify that in the multi-beacon aided AUV integrated navigation system, compared with the conventional LBL and EKF, UKF provides more stable and accurate position information in real time.

This work addresses the implementation of the navigation, guidance and control of autonomous underwater vehicles on mobile acoustic networks. After a theoretical discussion of the algorithms required for such operation, we present results from simulation and real experiments that validate the proposed solutions.

Accurate positioning is essential for the navigation of autonomous underwater vehicles (AUVs), especially during docking and homing. This presentation explores the feasibility of using passive Acoustic Identification (AID) tags for precise AUV localization at a docking station. As a proof of concept, scaled experiments used AID tags made of four concentric hemispherical acrylic layers, each creating a unique backscattered acoustic signature when ensonified by a broadband ultrasonic transducer in GaTech water tank. The tags in these tests have a 9-in. outer diameter and were tested at a frequency range of 700 kHz to 1.3 MHz over a distance of up to 6 m. To enhance detection in cluttered environments, a parameterized signal processing method was employed. Additionally, we will present analysis of long-range experiments to assess the tags' detectability at greater distances using larger AID tags with outer diameter of up to 24-in. outer diameter. These tests will operate at a frequency range of 100 to 600 kHz, aiming to reach distances of tens of meters. Implications for the performance of AID tags operating under real-life conditions will be discussed.

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception.

Autonomous underwater vehicle (AUV) navigation and localization is crucial and challenging in Global Positioning System-denied environments. AUV carrying synthetic aperture sonars (SASs) can provide precise motion estimation of the platforms. This study focuses on enhancing the performance of AUV localization using dual-sided SASs. The two-dimensional velocities of AUV are estimated using dual-sided SAS echoes. An extended Kalman filter (EKF) is then adopted for dead reckoning using the estimated velocities. The experimental results demonstrate that the proposed method has better localization accuracy than that of the Doppler velocity log (DVL).