Adaptive path-planning for AUVs in dynamic underwater environments using sonar data

Abstract

This paper presents an innovative approach to path-planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments, leveraging single-beam sonar data. Recognizing the limitations of traditional sonar systems in providing detailed environmental data, we introduce a method to effectively utilize Ping360 sonar scans for obstacle detection and avoidance. Our research addresses the challenges posed by dynamic underwater currents and obstacle unpredictability, incorporating environmental factors such as water temperature, depth, and salinity to adapt the sonar's range detection capabilities. We propose a novel algorithm that extends beyond the capabilities of the A* algorithm, considering the underwater currents' impact on AUV navigation. Our method demonstrates significant improvements in navigational efficiency and safety, offering a robust solution for AUVs operating in uncertain and changing underwater conditions. The paper outlines our experimental setup, algorithmic innovations, and the results of comprehensive simulations conducted in a controlled tank environment, showcasing the potential of our approach in enhancing AUV operational capabilities for defense and security applications.