Kinodynamic Motion Model-Based MPC Path Planning and Localization for Autonomous AUV Teams in Deep Ocean Exploration

Autonomous underwater vehicles (AUVs) are increasingly used for deep-sea exploration, often operating in teams coordinated by an unmanned surface vehicle (USV). The USV acts as a central hub for communication, navigation, and mission coordination, enabling efficient and safe ocean exploration. However, two critical challenges must be addressed: path planning and localization. The lack of GPS and the limitations of RF communication underwater make localization particularly challenging, relying instead on acoustic-based navigation, which is constrained by short operational ranges. This paper proposes a localization-aware model predictive control (MPC)-based path-planning strategy for a team of AUVs under USV supervision. The approach optimizes AUV trajectories to enhance energy efficiency, maximize exploration coverage, and maintain localization accuracy. AUVs communicate with the USV and each other via sonar-based acoustic signals, requiring them to stay within the operational sonar range. The proposed kinodynamic MPC-based algorithm dynamically adjusts AUV motion trajectories while mitigating inertial navigation errors, ensuring robust and efficient underwater exploration.