Course Control of a Manta Robot Based on Amplitude and Phase Differences

Three-Dimensional Numerical Study of Hydrodynamic Interactions between Pectoral Fins and the Body of Aquatic Organisms

Fish swimming has attracted attention as a locomotion system with excellent propulsive efficiency. They swim by moving their body, fins, and other organs simultaneously, which developed during evolution. Among their many organs, the pectoral fin plays a crucial role in swimming, such as forward-backward movement and change of direction. In order to investigate the hydrodynamic interaction between pectoral fins and fish bodies, we examined the asymmetric flapping motion of the pectoral fin concerning the body axis and investigated the effect of the pectoral fin on the propulsive performance of the body of a small swimming object by numerical simulation. In this study, the amplitude ratio, frequency ratio, and phase of the body and pectoral fin varied. Therefore, although propulsive performance increased in tandem with the frequency ratio, the amplitude ratio change had negatively affected the propulsive performance. The results revealed that the propulsive performance of the fish was high even in low-frequency ratios when the phase difference was varied. The highest propulsion efficiency increased by a factor of about 3.7 compared to the phase difference condition of 0.

Control of movement of underwater swimmers: Animals, simulated animates and swimming robots

The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations.

A Bioinspired Cownose Ray Robot for Seabed Exploration

This article presents the design and the experimental tests of a bioinspired robot mimicking the cownose ray. These fish swim by moving their large and flat pectoral fins, creating a wave that pushes backward the surrounding water so that the fish is propelled forward due to momentum conservation. The robot inspired by these animals has a rigid central body, housing motors, batteries, and electronics, and flexible pectoral fins made of silicone rubber. Each of them is actuated by a servomotor driving a link inside the leading edge, and the traveling wave is reproduced thanks to the flexibility of the fin itself. In addition to the pectoral fins, two small rigid caudal fins are present to improve the robot's maneuverability. The robot has been designed, built, and tested underwater, and the experiments have shown that the locomotion principle is valid and that the robot is able to swim forward, perform left and right turns, and do floating or diving maneuvers.

A Novel Integrated Gliding and Flapping Propulsion Biomimetic Manta-Ray Robot

Bionic underwater robots are the intersection of biology and robotics; they have the advantages of propulsion efficiency and maneuverability. A novel vehicle that combines a gliding and flapping propulsion inspired by a manta ray is presented in this article. The outstanding character of the robot is that its integrated maneuverable flapping propulsion relies on two bionic flexible pectoral fins and long-range efficient gliding propulsion, which is based on a buoyancy-adjustment system and a mass-adjustment system. We designed the biomimetic manta ray robot and analyzed the principle of the gliding and flapping system in this paper. The gliding propulsion capability and the flapping propulsion performance are verified through gliding and swimming experiments. In conclusion, the designed bionic manta robot provides a platform with practical application capabilities in marine environment detection, concealed reconnaissance, and aquaculture.

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

The current development of manta ray robots is usually based on functional bionics, and there is a lack of bionic research to enhance the similarity of motion posture. To better exploit the characteristics of bionic, a similarity evaluation rule is constructed herein by a Dynamic Time Warping (DTW) algorithm to guide the optimization of the control parameters of a manta ray robot. The Central Pattern Generator (CPG) network with time and space asymmetry oscillation characteristics is improved to generate coordinated motion control signals for the robot. To optimize similarity, the CPG network is optimized with the genetic algorithm and particle swarm optimization (GAPSO) to solve the problems of multiple parameters, high non-linearity, and uncertain parameter coupling in the CPG network. The experimental results indicate that the similarity between the forward motion pose of the optimized manta ray robot and the manta ray is improved to 88.53%.

Bioinspired Central Pattern Generator and T-S Fuzzy Neural Network-Based Control of a Robotic Manta for Depth and Heading Tracking

Aiming at the difficult problem of motion control of robotic manta with pectoral fin flexible deformation, this paper proposes a control scheme that combines the bioinspired Central Pattern Generator (CPG) and T-S Fuzzy neural network(NN)-based control. An improved CPG drive network is presented for the multi-stage fin structure of the robotic manta. Considering the unknown dynamics and the external environmental disturbances, a sensor-based classic T-S Fuzzy NN controller is designed for heading and depth control. Finally, a pool test demonstrates the effectiveness and robustness of the proposed controller: the robotic manta can track the depth and heading with an error of ±6 cm and ±6°, satisfying accuracy requirements.

Design and Experimental Research on a Bionic Robot Fish with Tri-Dimensional Soft Pectoral Fins Inspired by Cownose Ray

Bionic propulsion has advantages over traditional blade propellers, such as efficiency and noise control. Existing research on ray-inspired robot fish has mainly focused on a single type of pectoral fin as bionic propeller, which only performed well in terms of pure speed or maneuverability. Rarely has the performance of different fin types been compared on the same platform to find an optimal solution. In this paper, a modularized robot fish with high-fidelity biomimetic pectoral fins and novel multi-DOF propelling mechanism is presented. A kinematic model of the pectoral fin based on motion analysis of a cownose ray is introduced as guidance for the propelling mechanism design. A high-fidelity parametric geo-model is established and evaluated based on statistical data. The design and fabrication process of the 3D soft bionic fins, as well as the robot platform, is also elaborated. Through experiments comparing the performance of different fin types constructed with different materials and approaches, it was found that the new soft fins made of silicon rubber have better performance than traditional fins constructed with a flexible inner skeleton and a permeable outer skin as a result of better 3D profile preservation and hydrodynamic force interaction. The robot ray prototype also acquires a better combination of high speed and maneuverability compared to results of previous research.