An autonomous coverage path planning algorithm for maritime search and rescue of persons-in-water based on deep reinforcement learning

Abstract

The prevalence of maritime transportation and operations is increasing, leading to a gradual increase in drowning accidents at sea. In the context of maritime search and rescue (SAR), it is essential to develop effective search plans to improve the survival probability of persons-in-water (PIWs). However, conventional SAR search plans typically use predetermined patterns to ensure complete coverage of the search area, disregarding the varying probabilities associated with the PIW distribution. To address this issue, this study has proposed a maritime SAR vessel coverage path planning framework (SARCPPF) suitable for multiple PIWs. This framework comprises three modules, namely, drift trajectory prediction, the establishment of a multilevel search area environment model, and coverage search. First, sea area-scale drift trajectory prediction models were employed using the random particle simulation method to forecast drift trajectories. A hierarchical probability environment map model was established to guide the SAR of multiple SAR units. Subsequently, we integrated deep reinforcement learning with a reward function that encompasses multiple variables to guide the navigation behavior of ship agents. We developed a coverage path planning algorithm aimed at maximizing the success rates within a limited timeframe. The experimental results have demonstrated that our model enables vessel agents to prioritize high-probability regions while avoiding repeated coverage.