This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments.

Soft actuator has broad application prospects due to its good compliance to different environments. However, its deformation is difficult to be described by the traditional method, so it is impossible to establish an accurate model of its motion, resulting in the difficulty of motion control of the software actuator. In this study, a soft robotic fish is designed, and a motion modeling method is proposed applying the neural network. The neural network-based motion model of the water-actuated soft robotic fish is constructed through neural network training with data collected by visual sensor. Further, a data set of control signals about the desired swing angle of robotic fish is established based on the motion model and stochastic algorithm, and the accurate motion control of the robot is implemented. The accuracy of the motion control method and the free swimming ability of the soft robotic fish using the control method in the water are analyzed quantitatively and qualitatively through the static and dynamic swing experiments of the robotic. This study provides a new idea for the motion modeling of soft actuators, which can effectively promote the development of modeling methods and theories of soft robots.

Parameter uncertainties and unknown disturbances always exist in the trajectory tracking control of a deep-sea hydraulic manipulators (DHMs), significantly reducing tracking accuracy. To address these issues, a practical finite-time observer-based adaptive backstepping super-twisting sliding mode control method (PFTO-ABSTC) is proposed for precise DHM tracking control. First, a projection-type adaptive law is constructed to handle the parameter uncertainties. In addition, Levant’s Differentiator is employed to obtain velocity of the DHM and construct the adaptive law regression vector, minimizing system noise from differentiation and filtering operations. Second, a practical finite-time observer (PFTO) strategy is proposed to estimates lump disturbances and compensates them in the controller, avoiding high-gain phenomenon of the controller effectively. Then, the PFTO-ABSTC controller is proposed by integrating the backstepping technique, which chattering-free property can be achieved. In addition, the uncertainties and disturbances of the DHM dynamics are well addressed in the closed-loop system, and asymptotic tracking performance can be guaranteed by Lyapunov theory. Finally, comparative experimental results validate the effectiveness of the proposed control strategy. Experimental results show that the proposed method can achieve smaller control errors and better control performance than the ESO-ABSTC, ABSTC, and PID control methods, and the maximum control errors are improved by at least 41.30%, 50.76%, and 66.67%, respectively.