Abstract
Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.
Highlights
There is growing appeal for autonomous systems in multiple fields, including manufacturing, transportation, routine work, and work in dangerous environments
Some elements of path planning and collision avoidance are common across congested waters and open sea, we are mainly concerned with shorter time frames and congested waters in the papers we study here
We wish to point out that, according to our knowledge, several other path planning algorithms used for mobile robots, ground vehicles, aerial vehicles, or underwater vessels have not been applied to surface vessels yet, e.g., bug algorithm [64], Voronoi fast marching method [65], symbolic wavefront expansion [66], probabilistic roadmaps [67], and fast marching* (FM*) [68]
Summary
There is growing appeal for autonomous systems in multiple fields, including manufacturing, transportation, routine work, and work in dangerous environments. In an accompanying article in this journal [1], we present a review on theory and methods for path planning and collision avoidance of ASVs. We attempt to unify and clarify relevant terminology and concepts such as autonomy and safety, as well as models for guidance, navigation, and control. We extend this scheme to classify state-of-the-art algorithms presented in 45 different peer-reviewed scientific papers. 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. Some elements of path planning and collision avoidance are common across congested waters and open sea, we are mainly concerned with shorter time frames and congested waters in the papers we study here
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