Autonomous surface vessels (ASVs) have started to operate in many safety-critical scenarios where rich sensor information is required for situational awareness, environmental perception, motion planning, collision avoidance and navigational control. A timely diagnosis of faulty onboard sensors is therefore essential for ensuring maritime safety and reliability. In this paper, a model-based fault diagnosis scheme is presented for ASVs affected by multiple sensor faults. Various monitoring modules comprising nonlinear observers are employed for the detection of faults occurring in the vessel’s navigational sensors. Further, multiple fault isolation is performed based on a combinatorial decision logic, achieved by grouping the available sensors into multiple sensor sets. The efficacy of the proposed scheme is illustrated through a simulation example of a vessel trajectory tracking scenario. It demonstrates the scheme’s ability to effectively isolate multiple fault combinations impacting the sensors considered.@en

Autonomous surface vessels (ASVs) increasingly gain appeal in the maritime industry for their high efficiency and improved navigational capabilities. However, risks originating from various internal and external factors such as faults, traffic, harsh weather conditions, etc., can affect their guidance and control capabilities and impact nominal vessel operations. The existing risk mitigation methods mainly focus on the vessel's guidance system and do not consider unsafe actions due to the control system. In this paper, we propose a new method based on a partially observable Markov decision process (POMDP) model for the online risk mitigation of autonomous inland vessels. The POMDP model-based method utilizes information about situational awareness to assist the vessel's planning and control system in real-time decision-making during hazardous situations, thereby ensuring that the vessel remains in a minimum-risk condition. Based on the identified risk-influencing factors (RIFs), the transition probabilities are updated by a Bayesian belief network (BBN). A case study of an autonomous inland vessel navigating in a confined waterway is presented to demonstrate the capability of the proposed method.@en