High maneuverability is essential to the autonomous operation of underwater robots. To achieve real-time maneuvering motion, the control strategy must take into account nonlinear hydrodynamic effects, which are extremely difficult to accurately capture during motion and therefore a balance must be struck between accuracy and real-time computational efficiency. Therefore, this paper proposes a data-driven approach to model the dynamics of the underwater robot using Sparse Identification of Nonlinear Dynamics (SINDy). Compared with existing works, our method does not require any physical prior knowledge and only uses a short period of onboard sensor data. Subsequently, the learned dynamic model is incorporated into a model predictive controller (MPC) to enable precise attitude control. Finally, the proposed method is implemented on our developed fully vectored propulsion underwater robot, and a series of attitude tracking experiments are conducted in an indoor water tank. Experimental results reveal that our approach significantly improves the model accuracy and reduces the attitude tracking errors by over 79% at a control frequency of 20 Hz, which proves the effectiveness and real-time performance of the method.

Due to the low motion efficiency and maneuver-ability of underwater robots with six degrees of freedom, it is challenging for them to respond quickly to the attitude requirements during underwater autonomous manipulation. This paper presents a novel autonomous underwater robot with fully vectored propulsion and a model predictive control method to achieve more agile and efficient movements autonomously. In detail, we first design a robot with eight vector-distributed thruster layouts for fully vectored propulsion and construct the software architecture based on the robot operating system (ROS). Then, we establish the hydrodynamic model by adopting the Fossen approach and construct a 13-dimensional system state-space equation, which is discretized using the explicit fourth-order Runge-Kutta method. To achieve autonomous manipulation, model predictive control is employed along with physical constraints of the custom-built robot to enable real-time prediction and optimization of the robot’s states for control purposes. Finally, numerical simulations and experiments of the Point-to-Point Motion are conducted to test the robot’s performance. Experimental results reveal that the average error of each direction is 0.0027 m, 0.0031 m, and 0.0368 m in the x-axis, y-axis, and z-axis, respectively, and 0.8502°, 2.1941°, 0.2408° corresponding to three attitude angles, which verify the performance of employing MPC to control an autonomous underwater robot with fully vectored propulsion.