In the underwater domain, guaranteeing accurate navigation for an autonomous underwater vehicle (AUV) is a complex but fundamental task to be achieved. As a matter of fact, only by ensuring a correct AUV localization, it is possible to accomplish surveillance, monitoring, and inspection missions. Most of the navigation filters for AUVs are based on Bayesian estimators, such as the Kalman filter, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), and employ different instruments, including the Doppler velocity log to perform the localization task. Recently, the use of payload sensors, such as cameras or forward-looking SONARs, in navigation-aiding has arisen as an interesting research field in the attempt to reduce the localization error drift. Such sensors, if used simultaneously, can provide multiple observations, which can be combined in a Kalman filtering framework to increase navigation robustness against noise sources. Navigation techniques that employ multiple devices can provide a high improvement of the estimation quality, but they can also cause an increase in terms of computational load. Consequently, strategies that can represent a tradeoff between these two conflicting goals have to be investigated. In this contribution, two different frameworks have been implemented and compared: on the one hand, a centralized iterative UKF-based navigation approach and, on the other hand, a sensor fusion framework with parallel local UKFs. The sequential (or iterated) UKF, where the correction step is iteratively performed for each available measurement, belongs to the first class of filters. The federated and the consensus-based decentralized UKFs can be categorized as the second class and they differ in the employed fusion strategy. Experimental navigation data obtained during sea trials performed at Vulcano Island, Messina, Italy has been used for offline validation. The results analysis focuses on both the navigation quality and the filter robustness against the reduction of the available measurements.

Autonomous Underwater Vehicles (AUVs) are used by the scientific community for various applications, from collecting well‐distributed high‐quality data to mapping the seafloor or exploring unknown areas. Nonpredictable environmental conditions and sensor acquisitions make the design of AUV surveys challenging even for expert operators. Multiple attempts are required, and the collected data quality is not guaranteed: The AUV passively stores the sensors' acquisitions that are then analyzed offline after its recovery. In Forward‐Looking SONAR (FLS) seabed inspections, the vehicle follows lawnmower paths designed by an expert operator that considers the sensor characteristics. The performance of FLSs is affected by several environmental conditions and possible protruding objects. This paper presents a probabilistic framework for FLS‐based seabed inspections that endow the AUV with the ability to autonomously conducting the survey and ensure adequate coverage of the target area. A three‐dimensional probabilistic occupancy mapping system for FLS reconstructions to update the covered area map was developed. The map is used by the Coverage Path Planning (CPP) algorithm to evaluate the visibility of the viewpoints that are generated as nodes of a random tree. The Next‐Best Viewpoint (NBV) is selected as the first node in the branch expected to collect more data, and the path to reach the NBV is computed using the rapidly exploring random tree (RRT*) algorithm. The sensor‐driven coverage approach is used in a receding‐horizon manner. The proposed Receding‐Horizon Coverage Approach was validated with simulations and real prerecorded data. Finally, the framework was used online during an experimental campaign where several FLS seabed inspections were performed.

Seabed inspection is one of the most sought-after applications for autonomous underwater vehicles (AUVs). Acoustical sensors, such as side-scan sonars and forward-looking sonars (FLSs), are commonly favored over optical cameras to carry out such a task. Indeed, sonars are not influenced by illumination conditions and can provide high-range data. However, due to the lack of features and low resolution, acoustical images are often hard to interpret with conventional automatic techniques, forcing human operators to analyze thousands of collected images to identify the so-called objects of potential interest (OPIs). In this article, we report the development of an automatic target recognition (ATR) methodology to identify and localize OPIs in FLS imagery. Such detections have been then exploited to realize a virtual world model with the probabilistic multiple hypothesis anchoring data association and model tracking algorithm. Distinct models of convolutional neural networks have been trained with a data set acquired in May 2019 at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale—CSSN) basin in La Spezia, Italy. The ATR strategy has been successfully validated offline with the data gathered in October 2019 in the same site where the seabed targets were replaced and relocated. As regards the world modeling technique, it has been preliminarily tested on a simulated scenario built upon unmanned underwater vehicle Simulator. Finally, both the ATR and world modeling systems were on-field tested in October 2020 at the CSSN basin in a multivehicle architecture by employing an acoustical channel between FeelHippo AUV and an autonomous moving buoy.

Coastal zones are subjected to a wide range of phenomena acting on very different temporal and spatial scales: from decades to days and from hundreds of kilometers to tens of meters. Planning the management of such areas, thus, requires an accurate and updated knowledge of the ongoing processes. While standard monitoring activities are functional for the medium-long time scale and medium-large spatial scale, they struggle to provide adequate information concerning the short period (i.e., days) and small range (i.e., few meters). In addition, such operations are affected by high costs and logistic complexity since they generally involve the deployment of specific aircraft or maritime vehicles. On the contrary, the employment of robotic devices can represent a solution to these issues. Their proper use can allow for frequent surveys and enhance the coverage of the acquired data due to optimized mission strategies. Marine robotics has the potential to arise as an efficient complementary tool to standard monitoring techniques. Nevertheless, the use of marine robots is still limited and should be improved. The purpose of this paper is to discuss the current state of robotic technology, identifying both the benefits and shortcomings of its use for micro-tidal marine-coastal monitoring. The discussion will be supported by actual results, taken as an example, achieved using FeelHippo AUV, the compact Autonomous Underwater Vehicle (AUV) developed by the Department of Industrial Engineering at the University of Florence, Italy.