A Dynamic Programming Approach to the Collision Avoidance of Autonomous Ships

Raphael Zaccone ()
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Raphael Zaccone: Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecompunicazioni (DITEN), Scuola Politecnica, Università degli Studi di Genova, Via Montallegro 1, 16145 Genova, Italy

Mathematics, 2024, vol. 12, issue 10, 1-22

Abstract: The advancement of autonomous capabilities in maritime navigation has gained significant attention, with a trajectory moving from decision support systems to full autonomy. This push towards autonomy has led to extensive research focusing on collision avoidance, a critical aspect of safe navigation. Among the various possible approaches, dynamic programming is a promising tool for optimizing collision avoidance maneuvers. This paper presents a DP formulation for the collision avoidance of autonomous vessels. We set up the problem framework, formulate it as a multi-stage decision process, define cost functions and constraints focusing on the actual requirements a marine maneuver must comply with, and propose a solution algorithm leveraging parallel computing. Additionally, we present a greedy approximation to reduce algorithm complexity. We put the proposed algorithms to the test in realistic navigation scenarios and also develop an extensive test on a large set of randomly generated scenarios, comparing them with the RRT* algorithm using performance metrics proposed in the literature. The results show the potential benefits of an autonomous navigation or decision support framework.

Keywords: dynamic programming; autonomous ship; path planning; collision avoidance; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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