Adaptive Autonomous Underwater Vehicles Research Articles

Fuzzy neural network adaptive AUV control based on FTHGO

ABSTRACT A fuzzy radial basis function neural network (Fuzzy RBFNN) adaptive control scheme, based on fixed-time high gain state observer (FTHGO), is proposed to address the unpredictability of real-time state and composite interference in the trajectory tracking of the fully driven Autonomous Underwater Vehicle (AUV), ensuring fixed-time system convergence regardless of initial conditions. Firstly, a fixed-time backstepping controller is designed and a first-order fixed-time filter is introduced to tackle the differential explosion issue. Secondly, an FTHGO is developed to observe the real-time states of the AUV without assuming global known state signal. Then, the composite interference in the AUV system is effectively compensated by integrating the Fuzzy RBFNN technique. Finally, the fixed-time stability of the entire closed-loop system is proven utilising the Lyapunov stability theory. The effectiveness of the proposed algorithm is proved by the simulation experiment.

Adaptive Autonomous Underwater Vehicles: An Assessment of Their Effectiveness for Oceanographic Applications

Autonomous underwater vehicles (AUVs) are practical tools for ocean observation. However, they tend to operate in an automatic rather than autonomous way. This reflects the attitudes and behaviors that individuals and organizations share when adopting new technology in this industry. This paper clarifies the factors that are preventing one important aspect of autonomy—adaptive mission planning (AMP)—from transitioning from research to commercial and bespoke AUVs. A total of 25 experts comprising AUV developers and users, with combined 237 years of experience, provided their views in a structured survey covering several different hypotheses. There is insufficient evidence to determine clearly a single reason for failure to adopt AMPs, but a primary cause is the paucity of demonstration trials. This view is irrespective of participants' years of experience. Managers, engineers, and technologists agree on the two most likely causes for failure to adopt AMPs. However, the differences between the assessments provided by researchers and these three professional groups are statistically significant, with p value <0.005. For researchers, complexity is one of the two most important inhibitory factors. We present recommendations to support the integration of AMP into AUVs substantiated by recent examples where government, industry, and researchers have developed and tested AMPs.

Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

Fast Target Detection in Synthetic Aperture Sonar Imagery: A New Algorithm and Large-Scale Performance Analysis

In this paper, a new unsupervised algorithm for the detection of underwater targets in synthetic aperture sonar (SAS) imagery is proposed. The method capitalizes on the high-quality SAS imagery whose high resolution permits many pixels on target. One particularly novel component of the method also detects sand ripples and estimates their orientation. The overall algorithm is made fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the approach makes near-real-time detection of proud targets in sonar data onboard an autonomous underwater vehicle (AUV) feasible. No training data are required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that are collected in situ. To validate and assess the performance of the proposed detection algorithm, a large-scale study of SAS images containing various mine-like targets is undertaken. The data were collected with the MUSCLE AUV during six large sea experiments, conducted between 2008 and 2012, in different geographical locations with diverse environmental conditions. The analysis examines detection performance as a function of target type, aspect, range, image quality, seabed environment, and geographical site. To our knowledge, this study-based on nearly 30 000 SAS images collectively covering approximately 160 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of seabed, and involving over 1100 target detection opportunities-represents the most extensive such systematic, quantitative assessment of target detection performance with SAS data to date. The analysis reveals the variables that have the largest impact on target detection performance, namely, image quality and environmental conditions on the seafloor. Ways to exploit the results for adaptive AUV surveys using through-the-sensor data are also suggested.

Concurrent detection and localization of buried targets using track-before-detect processing

The GOATS (Generic Oceanographic Array Technology Systems) Joint Research Program explores the development of environmentally adaptive autonomous underwater vehicle (AUV) technology for Rapid Environment Assessment and Mine Counter Measurement (MCM) in coastal environments. MIT is developing the GOATS multistatic sonar concept which uses a low-frequency source on one AUV to subcritically insonify the seabed over a wide area, while a formation of multiple AUVs are used for mapping the associated 3-D scattered acoustic field in the water column. Exploring the different characteristics of the scattering from various buried targets, an algorithm has been developed for autonomous, concurrent detection and localization of buried targets. At the subcritical isonification angles necessary to sustain high coverage rate, the buried target detection depends on the measurement of very weak signals, in general undetectable by traditional detect-before-track algorithms. The method developed here applies a Track-Before-Detect (TBD) approach to solve this problem. This technique tracks the AUV trajectory first using the slowly changing environment information, and then the weak signal detection is declared after the AUV track is estimated at sufficient level of confidence. This new algorithm has been applied on the GOATS-2000 bistatic data. The result shows the successful navigation results by confident detection of three buried targets, two of which are particularly weak scatterers. A real-time, autonomous implementation of the algorithm was recently demonstrated in the GOATS-2002 experiment, and the result for both experiments will be discussed. [Work supported by ONR.]