NeuroFuzzy Dynamic Position Prediction for Autonomous WorkClass ROV Docking This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive NeuroFuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, workclass ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on humanintheloop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFISbased method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on realworld reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a workclass ROV with a cage type TMS, deployed using an Aframe launch and recovery system.

Learningbased Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats This paper presents a Learningbased Nonlinear Model Predictive Control (LBNMPC) algorithm for reconfigurable autonomous vessels to facilitate highaccurate path tracking. Each vessel is designed to latch to a predefined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a realtime parameter estimator and a feedback control strategy, which is capable of ensuring highaccurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated online and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve pathtracking performance. Through experiments on different configurations of connectedvessels, we demonstrate stability of our proposed approach and its effectiveness in highaccuracy in path tracking.