Always regarding the issue of USV obstacle avoidance by using AIS, [PERSON] and [PERSON] [4] in their work suggest using a Deep Learning technique based on a double-gated recurrent unit neural network (GRU-RNN) for making appropriate automatic collision-avoidance decisions. Also, in this case the neural networks were trained on the basis of real data, related to successful collision-avoidance events, obtained from the Thianjin port. On the other hand, [PERSON] et al. [5] describe the use of an Improved Genetic Algorithm (IGA) optimized Radial Basis Function (RBF) neural network for an effective application to the tracking control of an USV. For evaluating the performance of the proposed algorithm it is compared to two other ones based on a Fuzzy neural network and a simple RBF network respectively. Finally, [PERSON] et al. [6] show as an Improved Hierarchical Deep Q Network (HDQN) can be used to perform the 3D path planning of an AUV. The results obtained by simulations and at-field experiments demonstrate the effectiveness of the methodology. The problem of the path-following for USVs is investigated by [PERSON] et al. [7] which propose a robust output feedback methodology based on a hierarchical control structure using a finite-time Line Of Sight (LOS) guidance algorithm and an (Extended State Observer) ESO-based output feedback backstepping controller. A global stability analysis is carried out based on Lyapunov theory and numerical simulations show the fast convergence of the proposed guidance law. [PERSON] et al. [8] in their paper describe the use of a Double Layered Hybrid Multi-Robot framework for guidance and navigation of USVs. After a discussion of the different approaches previously proposed in the literature for solving the problem of multi-agent USV path guidance they suggest the use of a constrained A' path planner with a Virtual Target (VT) combined with the use of a potential field-based swarm aggregation technique for the cooperative navigation of multi-USVs. Some of the results obtained in the framework of the EU FP7 \"Caddy--Cognitive Autonomous Diving Buddy\" project, where a set of control algorithms were developed to be used by an AUV diving together with a diver and cooperating, are presented by [PERSON] et al. [9]. The benefit of the presented algorithms is the joining of diver following and guiding into a single seamless controller without leader/follower role exchange. The authors evaluated the performance of the proposed algorithms carrying out experiments in a pool. [PERSON] et al. [10] developed a model identification method for AUVs with multi-propellers based on Computational Fluid Dynamics (CFD) calculation and maximum likelihood algorithm. The model obtained by this method has got a high accuracy as far as the resistance coefficient and turning-related parameters are concerned while the accuracy related to other parameters, e.g., lateral motion, is lower. The work was evaluated by means of experimental tests. [PERSON] et al. [11] in their article describe the complete modeling and control design for maneuvering a twin water-jetted USV along a desired path. First, the authors developed a 3-DOF mathematical model of the USV together with an integrated control system for verifying the maneuverability of the proposed vehicle. Secondly, they designed and implemented a Human Simulated Intelligent Control (HSIC) controller and a LOS (Line Of Sight) algorithm. Both the HSIC and the LOS systems were verified with at-field tests. [PERSON] et al. [12] face the problem of the dynamic modeling and the motion simulation of an unmanned ocean platform composed of a USV and a UUV connected by means of an underwater cable. The developed motion equations, derived from Newton second law and on the lumped-mass method, take in account also wind and currents disturbances. From the analysis of these equations some useful suggestions for the design of a real platform can be derived. Finally, the work presented by [PERSON] et al.