Adaptive vessel navigation for purse seine fishing net deployment

Abstract

This paper presents an innovative framework for adaptive navigation of purse-seine vessels during the deployment of fishing nets. Firstly, the framework leverages geometry based real-time fish net shape estimation using sparse acoustic and GPS positioning sensors embedded within the net. This shape estimation is then utilized to predict the optimal path for vessel navigation, ensuring efficient and precise net deployment. By integrating real-time observations and graphical optimization techniques, the proposed method addresses practical challenges in uncertain marine environments such as adapting the navigation path plan to the inherent variability in fish school behaviour and ocean currents. The approach is validated through simulated fishing net deformation scenarios with Blender software, demonstrating its capability to maintain the operational efficiency and adaptation to environmental uncertainties.