Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control task requires the fish to keep a desired angle of attack in an unknown and even varied incoming flow. To this end, we propose a learning-based approach by using a deep neural network directly maps the raw data of sensors equipped on the robot to the continuous control signals in an end-to-end manner. First, based on experimental data of the physical robot, we construct a data-driven simulation environment including three modules of dynamic, sensor, and control. The dynamic and sensor modules are established to model the dynamics of the fish and to generate its sensors’ data, based on which a deep reinforcement learning (DRL) algorithm in the control module is trained to get a control policy. Then, we directly deploy the trained policy to a physical robotic fish for attitude holding task. Experimental results demonstrate the robustness and effectiveness of the DRL policy and, thus, verify the success of our approach to achieving sim-to-real transfer.

Lateral line system (LLS) is a sensory organ system which serves functions in varieties of flow-relative fish behaviors. Inspired by excellent performances of LLS in fish behaviors, multiple artificial lateral line systems (ALLSs) have been designed and applied to promote underwater robot technology. In this article, we focus on using ALLS for longitudinal separation sensing between two adjacent swimming robotic fish whose tails oscillate, and meanwhile the two fish are towed with a precisely controlled speed which equals to the rectilinear speed of freely-swimming robotic fish with the same oscillating parameters. Flow visualizations based on dye injection technique, hydrogen bubble technique, and computational fluid dynamics simulation are conducted to study the distribution characteristics of the vortices caused by the robotic fish. In addition, towing tank experiments are conducted for investigating the qualitative relationships between the longitudinal separations and the ALLS-measured HPVs of the two robotic fish. This work provide a guidance for the application of ALLS in relative states sensing of underwater robot group composed of two or more individuals, which has been rarely explored.