Attitude estimation is a crucial aspect for navigation and motion control of autonomous vehicles. This concept is particularly true in the case of unavailability of localization sensors when navigation and control rely on dead reckoning strategies; in this case, indeed, the orientation estimate is also used along with speed measurements to update the position estimate. Among the different approaches proposed in the literature, the de facto state of the art in this field is represented by nonlinear complementary filters: they fuse the measurements of angular rate obtained through gyroscopes, and a measurement of gravity and Earth's magnetic field vectors respectively obtained through accelerometers and magnetometers. This paper is focused on an attitude estimation strategy for autonomous underwater vehicles (AUV). The proposed novelty includes the identification of some critical issues that arise when AUV attitude estimation algorithms are applied in practice. They are mainly due to the use of low-accuracy low-cost microelectromechanical systems (MEMS) sensors and on different sources of magnetic disturbances. Some strategies to overcome the identified issues are proposed, including the integration of a single-axis fiber optic gyroscope (FOG) that ensures a considerable performance improvement with a moderate cost increase. The proposed strategies for detection of issues and sensor fusion have been experimentally tested and validated in a real application scenario estimating the attitude of an AUV performing a lawn mower path. The expected performance improvement is confirmed; the obtained results are described and analyzed in this paper.

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.

ARchaeological RObot systems for the World's Seas (ARROWS) EU Project proposes to adapt and develop low-cost Autonomous Underwater Vehicle (AUV) technologies to significantly reduce the cost of archaeological operations, covering the full extent of archaeological campaign. ARROWS methodology is to identify the archaeologists requirements in all phases of the campaign and to propose related technological solutions. Starting from the necessities identified by archaeological project partners in collaboration with the Archaeology Advisory Group, a board composed of European archaeologists from outside ARROWS, the aim is the development of a heterogeneous team of cooperating AUVs capable of comply with a complete archaeological autonomous mission. Three new different AUVs have been designed in the framework of the project according to the archaeologists' indications: MARTA, characterized by a strong hardware modularity for ease of payload and propulsion systems configuration change; U-C AT, a turtle inspired bio-mimetic robot devoted to shipwreck penetration and A-Size AUV, a vehicle of small dimensions and weight easily deployable even by a single person. These three vehicles will cooperate within the project with AUVs already owned by ARROWS partners exploiting a distributed high-level control software based on the World Model Service (WMS), a storage system for the environment knowledge, updated in real-time through online payload data process, in the form of an ontology. The project includes also the development of a cleaning tool for well-known artifacts maintenance operations. The paper presents the current stage of the project that will lead to overall system final demonstrations, during Summer 2015, in two different scenarios, Sicily (Italy) and Baltic Sea (Estonia)