The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robots. This study is focused on the development and 3-D path-following control of an agile robotic manta. First, a novel robotic manta with 3-D mobility is constructed, of which the distinctive pectoral fins provide the only propulsion. Specifically, the unique pitching mechanism is detailed in which the time-coupled coordination movement of the pectoral fins is applied. Second, based on a 6-axis force measuring platform, the propulsion characteristics of the flexible pectoral fins are analyzed. Then, the force-data-driven 3-D dynamic model is further established. Third, a control scheme combined with a line-of-sight (LOS) guidance system and a sliding-mode fuzzy controller is conceived, addressing the 3-D path-following task. Finally, various simulated and aquatic experiments are conducted, demonstrating the superior performance of our prototype and the effectiveness of the proposed path-following scheme. This study will hopefully generate fresh insights into the updated design and control of agile bioinspired robots performing underwater tasks in dynamic environments.

Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input–output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control.

Underwater visual localization is an essential technique for the autonomous operation of underwater robots. However, the unique underwater image characteristics, including refraction, sparse features, and severe noise, pose an enormous challenge to it. For addressing these issues, this paper proposes an open-source fiducial-based underwater stereo visual-inertial localization method under the extended Kalman filter (EKF) framework, which is called FBUS-EKF. First, the refraction is corrected by the refractive camera model and akin triangulation. Second, the fiducial marker and a novel marker pose estimation method are applied to alleviate the adverse effect of sparse features. Third, the EKF is utilized to fuse the inertial and visual information so as to reject the serious noise. Finally, extensive experiments on a test bench demonstrate the effectiveness of the FBUS-EKF method, where the typical localization error is less than 3%, namely, the average error is lower than 3 cm within one meter. The obtained results reveal that the FBUS-EKF method has the prospect to be applied in the precise short-range operation and the localization for underwater robots, which offers a valuable insight for further autonomous underwater task.

Implementing underwater target tracking remains difficult for a free-swimming robotic fish owing to the intrinsically reciprocating motion in fishlike propulsion. In this article, we present a novel robotic fish platform with a camera stabilizing system and achieve real-time two-dimensional target tracking assisted by reinforcement learning (RL) in continuous environments. More specifically, we first develop an active visual tracking system based on cascade control structure to obtain the relative orientation between the robotic fish and the underwater target. Then, we propose a target tracking controller dealing with continuous state and action spaces based on deep RL (DRL). The controller takes the position of the target object as input and yields the motion parameters of the bioinspired central pattern generator governed robotic fish. The robustness and adaptability of the proposed controller as well as the influence of time-delays on the control system are explored via simulated experiments under different scenarios. Finally, both static and dynamic tracking experiments on the actual robotic fish demonstrate the effectiveness of the proposed mechatronic design and control methods, providing insights to executing aquatic vision-based tracking tasks.