Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships

In this paper, we propose and demonstrate a method for risk-based path planning, generating risk-acceptable trajectories for an autonomous surface ship. This objective is achieved in two steps. The first step uses a dynamic probabilistic risk assessment method called K-shortest-paths Probabilistic Risk Assessment (KPRA), alongside risk models and dynamic system simulation, whose output is a list of paths ranked with respect to risk. Multiple risk influencing factors and accident types may be considered in this step. In this work, collision and powered grounding accidents, and their risk-influencing factors and indicators, are considered. The second step is to perform risk evaluation on the ranked scenarios, comparing them to predefined risk acceptance criteria and defining which scenarios have acceptable risk. Evaluating multiple paths allows operators to choose how strict the autonomous ship's decision-making should be regarding risk, avoiding the problem of minimization algorithms being too conservative in terms of risk. One of the risk-acceptable paths is then chosen as a local plan to be acted by the autonomous ship. This paper has two main scientific contributions. First, an online Bayesian belief network risk model for navigation accident probability is designed and integrated to improve KPRA. Second, we propose a modified guidance, navigation, and control framework for autonomous ships, to include risk-based planning. We implement the modified framework into a real autonomous surface vehicle (ASV) named Grethe and demonstrate the method's capabilities in field trials, where the ASV uses the method to generate local plans that avoid possible collision and grounding accidents.