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.

Modern mobile robots require precise and robust localization and navigation systems to achieve mission tasks correctly. In particular, in the underwater environment, where Global Navigation Satellite Systems cannot be exploited, the development of localization and navigation strategies becomes more challenging. Maximum A Posteriori (MAP) strategies have been analyzed and tested to increase navigation accuracy and take into account the entire history of the system state. In particular, a sensor fusion algorithm relying on a MAP technique for Simultaneous Localization and Mapping (SLAM) has been developed to fuse information coming from a monocular camera and a Doppler Velocity Log (DVL) and to consider the landmark points in the navigation framework. The proposed approach can guarantee to simultaneously locate the vehicle and map the surrounding environment with the information extracted from the images acquired by a bottom‐looking optical camera. Optical sensors can provide constraints between the vehicle poses and the landmarks belonging to the observed scene. The DVL measurements have been employed to solve the unknown scale factor and to guarantee the correct vehicle localization even in the absence of visual features. Furthermore, to evaluate the mapping capabilities of the SLAM algorithm, the obtained point cloud is elaborated with a Poisson reconstruction method to obtain a smooth seabed surface. After validating the proposed solution through realistic simulations, an experimental campaign at sea was conducted in Stromboli Island (Messina), Italy, where both the navigation and the mapping performance have been evaluated.

In the new era of Industry 4.0, digitization and automation are the tipping points for addressing rapidly evolving challenges. The momentum of this movement has underscored the critical importance of developing new technologies to collect, understand and use the enormous amount of data created. This revolutionary wave has also taken root in the healthcare sector, giving rise to the so-called Health 4.0, based on smart machines to provide patients with better, more value-added and more efficient healthcare services. This scenario has contributed to making innovative robot-augmented therapies a hot research topic with potentially significant societal impact. In this context, the authors present the implementation of a new prototype of a kinaesthetic exoskeleton to enhance and recover sensorimotor skills to perform grasping and manipulation via force stimuli from serious rehabilitation games. The discussion includes an initial overview of the system's hardware, both from a mechanical and electronic point of view, motivating the choices made in the design phase. It continues by moving on to a description of the system's low-level and high-level software architecture, along with the control strategy applied to implement progressive resistant force therapy. Finally, an example of serious game was developed as an application case for evaluating the system's usability and extrapolating empirical parameters indicative of responsiveness and accuracy. The results showed that the proposed system can reproduce constant forces with an average error of 0.75 N and standard deviation of 0.3 N with an average reaction time of 0.5 s and standard deviation of 0.4 s.