A methodology for automatic simulation-based testing of control systems for autonomous vessels is proposed. The work is motivated by the need for increased test coverage and formalism in the verification efforts. It aims to achieve this by formulating requirements in the formal logic Signal Temporal Logic (STL). This enables automatic evaluation of simulations against requirements using the STL robustness metric, resulting in a robustness score for requirements satisfaction. Furthermore, the proposed method uses a Gaussian Process (GP) model for estimating robustness scores including levels of uncertainty for untested cases. The GP model is updated by running simulations and observing the resulting robustness, and its estimates are used to automatically guide the test case selection toward cases with low robustness or high uncertainty. The main scientific contribution is the development of an automatic testing method which incrementally runs new simulations until the entire parameter space of the case is covered to the desired confidence level, or until a case which falsifies the requirement is identified. The methodology is demonstrated through a case study, where the test object is a Collision Avoidance (CA) system for a small high-speed vessel. STL requirements for safety distance, mission compliance, and COLREG compliance are developed. The proposed method shows promise, by both achieving verification in feasible time and identifying falsifying behaviors which would be difficult to detect manually or using brute-force methods. An additional contribution of this work is a formalization of COLREG using temporal logic, which appears to be an interesting direction for future work.

The main motivation for writing this article is to develop a model library for an All-Electric Ship that gives an opportunity to simulate both existing and new machinery systems without having to remodel the entire system each time. The model library should support the process of modelling and reuse, while also emphasizing openness to brace the modeller during the development and refinement phase. The bond graph approach is good when it comes to the physical modelling of systems and is a good tool for combining different energy domains to better help in understanding the system. In addition, a bond graph is a powerful method to find dependencies between various components. Using a causal analysis, any problems in the model, for example, algebraic constrains or dependent system variables, will be detected, and the necessary remodelling may be performed to handle such problems. The bond graph approach is therefore used when developing the component library. The component library consists of selected power producers such as diesel and gas engines, fuel cell and synchronous generator and power consumers such as asynchronous motor with a voltage source converter in addition to a generic load used for hotel and auxiliary loads. The library also consists of a ship model and propeller models.

We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of environmental forces. The method's efficiency is evaluated via simulations and sea trials, with the unmanned surface vehicle (USV) ReVolt performing three different tracking tasks: The four corner DP test, straight-path tracking and curved-path tracking. The results demonstrate the method's ability to accomplish the control objectives and a good agreement between the performance achieved in the Revolt Digital Twin and the sea trials. Finally, we include an section with considerations about assurance for RL-based methods and where our approach stands in terms of the main challenges.

The technological development ongoing in the maritime industry is making the ground for remotely and even autonomously operated vessels in the future. This is a result of increased data collection, processing and inter-connectivity capabilities. The industry is working towards increased safety, improved efficiency of the ship’s operation, improved environmental performance and a more cost-effective shipping. New technologies are developed in order to reach these goals, and DNV as a Class society is developing frameworks for assurance of such systems. The certification of ships and vessels with a high degree of automation or autonomy needs an increased focus on software, an understanding of the human-to-machine interaction and the resulting ability to solve complex operations in a secure way. In this paper, a method for high-level risk analysis of the safety aspects of autonomous vessels combined with automatic simulation-based testing of a control system, is proposed.