Autonomous Underwater Docking using Flow State Estimation and Model Predictive Control

We present a navigation framework to perform autonomous underwater docking to a wave energy converter (WEC) under various ocean conditions by incorporating flow state estimation into the design of model predictive control (MPC). Existing methods lack the ability to perform dynamic rendezvous and autonomously dock in energetic conditions. The use of exteroceptive sensors or high performing acoustic sensors have been previously investigated to obtain or estimate the flow states. However, the use of such sensors increases the overall cost of the system and expects the vehicle to navigate close to the seafloor or other landmarks. To overcome these limitations, our method couples an active perception framework with MPC to estimate the flow states simultaneously while moving towards the dock. Our simulation results demonstrate the robustness and reliability of the proposed framework for autonomous docking under various ocean conditions. Furthermore, we conducted laboratory trials with a BlueROV2 docking with an oscillating dock and achieved a greater than 70% success rate.