Research into path planning and collision avoidance (COLAV) algorithms for autonomous surface vehicles (ASVs) is motivated by continuing efforts to optimise operations and improve operational safety and performance. The general premise is that introducing higher levels of autonomy can reduce accidents, fuel costs, and operational costs (including crew), and improve regularity by reducing the frequency and consequence of human errors. To illustrate, the Annual Overview of Marine Casualties and Incidents 2019 [1] developed by the European Maritime Safety Agency (EMSA) states that in 2011–2018, more than 54% of all casualties with ships were navigational casualties—a combination of contact (15.3%), collision (26.2%) and grounding/stranding (12.9%) accidents. Moreover, from a total of 4104 accident events analysed during the investigations, 65.8% were attributed to human erroneous actions. Statistics also show that 41.7% of all casualties took place in port areas, followed by 27.4% in the coastal areas (territorial sea). These numbers indicate an increased collision risk when navigating in congested waters with several static and dynamic obstacles. The aforementioned high percentage of navigational casualties (54.4%) and attribution to human erroneous actions (65.8%) for human-controlled ships can likely be reduced by introducing autonomy in the operation of surface vessels. In addition, autonomous vessels are well suited for missions in dangerous and rough sea environments, for example by better real-time decision-making or in the case of unmanned vessels, removing the risk of human lives. On the other side, increased autonomy is also associated with several important challenges related to operation in open, coastal, and congested waters, energy consumption, environmental abnormalities, personnel requirements, and national security issues that need to be considered.
The autonomous ship market is expected to grow at a fast rate in the near future. According to Global Autonomous Ship and Ocean Surface Robot Market: Analysis and Forecast, 2018–2028, a market intelligence report by BIS Research [2], “the autonomous ship market in terms of volume is expected to grow at the rate of 26.7% during the period 2024–2035 and cumulatively generate a revenue of $3.48 billion by 2035.” Hence, we expect to see an increased demand for the development of autonomous systems technology in the maritime industry, and for ships in particular.
To enable safer systems on waters with increased autonomy requires development of improved and reliable guidance, navigation and control (GNC) systems. The focus of this paper is on guidance systems, and more precisely on path planning and collision avoidance algorithms. Looking at the research done in the field so far, it is of our interest to address the ambiguities in the terminology, investigate the regulatory framework associated with autonomous vessels, and decompose the GNC system of an ASV to review different types of path planning algorithms. Our research aims at summarising the main components that need to be considered when developing a path planning and/or collision avoidance algorithm, based on information available up to date. Whereas much of what we present is general across vessel size, other considerations will differ whether the vessel is a small boat or a large ship. In such cases, the reader should note that larger ships are our main focus.
The three main contributions of this paper can be summarised as follows: (i) an elucidation and clarification of terminology related to surface vessels and guidance systems; (ii) an analysis of the existing regulatory framework for ASVs; and (iii) a suggestion for classifying path planning algorithms. Thus, our work should be of interest for investigators and developers of intelligent algorithms for path planning and collision avoidance for ASVs. Indeed, in an accompanying article in this journal [3], we extend the classification scheme presented here, and analyse and classify algorithms presented in 45 different peer-reviewed scientific papers.
The remainder of this paper is organised as follows: Sect. 2 presents advantages, challenges, and current development of ASVs, defines terminology used within this scope, and provides an overview of previous survey papers. Section 3 details regulatory guidelines that define autonomy and control safety of ASVs. Section 4 presents the authors’ view on the GNC modules for ASV navigation, from the perspective of path planning and collision avoidance. Section 5 provides our proposed classification of path planning algorithms. Section 6 contains a discussion, and finally, some concluding remarks are drawn in Sect. 7.
