This paper presents a novel trajectory tracking control method for bioinspired undulating robotic systems with quadratic performance and input constraints. First, the relationship between the fin oscillation frequency and the generated thrust is established through dynamic analysis. Based on this, a constrained optimal tracking control framework is formulated by integrating duality theory with discrete-time dynamic programming. To solve the dual-constrained optimization problem, a learning algorithm is developed, which employs an exterior penalty function method combined with a simplex search strategy. The convergence precision is used to determine the optimal parameters, leading to an analytical expression for the control law. Finally, experimental results demonstrate the effectiveness and feasibility of the proposed control method.

Visuotactile sensors have been shown to provide rich contact information for robots. However, how to build a high-fidelity visuotactile simulator that supports multi-mode tactile imprints and various sensor configurations (such as coating patterns) remains a challenging problem. In this paper, we present TacFlex, an efficient and flexible simulator for visuotactile sensors, which physically simulates the elastomer deformation using Finite Element Methods (FEM), and focuses on linking the deformed elastomer mesh to diverse tactile imprints, including tactile images with arbitrary coating patterns and tactile 3D point clouds. We further propose a ray tracing-based rectification method to deal with multi-medium refraction effects to make the simulated tactile images more realistic. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness of TacFlex on several visuotactile sensors. Furthermore, we explore the Sim2Real performance of different tactile imprints provided by TacFlex in tactile perception and manipulation tasks, such as cylindrical object pose estimation and peg-in-hole. The perception/policy models trained in simulation are successfully deployed in the real world. Finally, we present the outlook on the potential of TacFlex in visuotactile manipulation learning. The TacFlex simulator is open-sourced to the community. See supplementary video, code, and results athttps://sites.google.com/view/tacflex/.

This paper presents a novel vision-based artificial lateral line (ALL) sensor, FlowSight, enhancing the perception capabilities of underwater robots. Through an autonomous vision system, FlowSight allows for simultaneous sensing the speed and direction of local water flow without relying on external auxiliary equipment. Inspired by the lateral line neuromast of fish, a flexible bionic tentacle is designed to sense water flow. Deformation and motion characteristics of the tentacle are modeled and analyzed using bidirectional fluid-structure interaction (FSI) simulation. Upon contact with water flow, the tentacle converts water flow information into elastic deformation information, which is captured and processed into an image sequence by the autonomous vision system. Subsequently, a water flow perception method based on deep neural networks is proposed to estimate the flow speed and direction from the captured image sequence. The perception network is trained and tested using data collected from practical experiments conducted in a controllable swim tunnel. Finally, the FlowSight sensor is integrated into the bionic underwater robot RoboDact, and a closed-loop motion control experiment based on water flow perception is conducted. Experiments conducted in the swim tunnel and water pool demonstrate the feasibility and effectiveness of FlowSight sensor and the water flow perception method.

This article presents the design and implementation of an innovative biomimetic underwater vehicle (BUV) and its locomotion controller. Through mimicking a dactylopteridae, the hybrid propulsion BUV is designed with two symmetrical bio-inspired long-fins and a double-joint fishtail. The mechatronic design of the dactylopteridae-inspired BUV with the pectoral long-fins and a double-joint fishtail is first provided. The two flexible long-fins compose the median and/or paired fin (MPF) propulsion, while the fishtail acts as the body and/or caudal fin (BCF) propulsion. Through the coordination of BCF and MPF propulsion modes, the BUV obtains excellent low-speed locomotion stability and also keeps high maneuverability. Moreover, the locomotion control methods based on central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID) are proposed for this BUV. In the end, the experimental results of the multimode motion and closed-loop motion control demonstrate the feasibility and effectiveness of the mechanism and the locomotion control system.Note to Practitioners—The motivation behind this article is the design of a novel biomimetic underwater vehicle (BUV) that possesses low-speed locomotion stability and fast swimming ability, which is suitable for carrying relevant sensors to complete water quality monitoring, biological observation, underwater equipment inspection, underwater structure detection, and other marine tasks. Currently, BUVs are usually designed as only one propulsion mode by caudal fin or paired fins, which makes it difficult to have the advantages of both modes. In order to further study the problem, we designed a dactylopteridae-inspired BUV with the bilateral pectoral long-fins (providing low-speed locomotion stability) and a double-joint fishtail (providing fast swimming ability). A hybrid-driven motion control framework is presented for the BUV based on a central pattern generators (CPGs) model and fuzzy adaptive proportion integral differential (PID). A series of experiments suggests that the mechanism and the locomotion control system are practical and valid. Hopefully, our mechanism and control framework can provide valuable theoretical and technical support guidance to the practicing marine engineer for the codesign of propulsion mode and control.