A robot fish is a type of bionic robot that has the shape and locomotion of a living fish. Most robot fish are designed to emulate living fish which use body-caudal fin (BCF) propulsion, and can be divided into three categories: single joint (SJ), multi-joint (MJ) and smart material-based "soft-body" design. Since the Massachusetts Institute of Technology first published research on them in 1989, there have been more than 400 articles published about robot fish. According to these reports, approximately 40 different types of robot fish have been built, with 30 designs having only the capability to flip and drift in water. The most important parts of researching and developing robot fish are advancing their control and navigation, enabling them to interact and "communicate" with their environment, making it possible for them to travel along a particular path, and to respond to commands to make their "fins" flap.
Design The basic biomimetic robotic fish is made up of three parts: a streamlined head, a body, and a tail.
The head is often made of a rigid plastic material (i.e. fiberglass) and contains all control units including a wireless communication module, batteries, and a signal processor. The body may be made of multiple jointed segments, which are connected by servomotors. Servomotors control the rotation angle of the joint. Some designs have pectoral fins fixed on both sides of the body to ensure stability in the water An oscillating caudal (tail) fin connected with joints and driven by a motor provides motive power.
Design inspiration
Engineers often focus on functional design. For example, designers attempt to create robots with flexible bodies (like real fish) that can exhibit undulatory motion. This kind of body enables the robot fish to swim similar to the way live fish swim, which can adapt and process a complicated environment. The first robot fish (MIT's RoboTuna) was designed to mimic the structure and dynamic properties of a Tuna. In an attempt to gain thrust and maneuvering forces, robot fish control systems are capable of controlling the body and caudal fin, giving them a wave-like motion. In order to control and analyze robotic fish movement, researchers study the shape, dynamic model and lateral movements of the robotic tail. One of the many tail shapes found on robot fish is lunate, or crescent shaped. Some studies show this kind of tail shape increases swimming speeds and creates a high-efficiency robot fish. The posterior tail creates thrust force, making it one of the most important parts of the robot fish. Living fish have powerful muscles that can generate lateral movements for locomotion while the head remains in a relatively motionless state. Thus, researchers have focused on tail kinematics when developing robot fish motion. Slender-body theory is often used when studying robot fish locomotion. The mean rate of work of the lateral movements is equal to the sum of the mean rate of work available for producing the mean thrust and the rate of shedding of kinetic energy of lateral fluid motions. The mean thrust can be calculated entirely from the displacement and swimming speed at the trailing edge of the caudal fin. This simple formula is used when calculating the locomotion of both robot and living fish. Realistic Propulsion Systems can help improve autonomous maneuvering and exhibit a higher level of locomotion performance. A diverse option of fins can be used in the creation of robot fish to achieve this goal. By including pectoral fins, robot fish can perform force vectoring and perform complex swimming behaviors instead of forward swimming only.
Control
The shapes and sizes of fins vary drastically in living fish, but they all help to accomplish a high level of propulsion through the water. In order for robot fish to achieve the same type of rapid and maneuverable propulsion, robot fish need multiple control surfaces. The propulsive performance is related to the position, mobility, and hydrodynamic characteristics of the control surfaces. The key to controlling a multi-joint robotic fish is creating a simplified mechanism that is able to generate a reasonable amount of control. Designers should consider some important factors, including lateral body motions, kinematic data and anatomical data. When designers mimic a BCF-type robot fish, the link-based body wave of the robot fish must provide motions similar to that of a living fish. This kind of body wave-based swimming control should be discrete and parameterized for a specific swimming gait. Ensuring swimming stability gait can be difficult, and transitioning smoothly between two different gaits can be tricky in robot fish. A central neural system known as a "Central Pattern Generator" (CPGs) can govern multilink robotic fish locomotion. The CPG is located in every segment, and can connect and stimulate contracting or stretching muscles. The cerebrum, the most anterior part of the brain in vertebrates, can control signal inputs to startup, stop and turn. After the systems form a steady locomotion, the signal from the cerebrum stops and the CPGs can produce and modulate locomotion patterns. Similar to their role in living fish, neural networks are used to control robot fish. There are several key points in the design of bionic neural networks. First, the bionic propeller adopts one servomotor to drive a joint while the fish has two group muscles in each joint. Designers can implement one CPG in each segment to control the corresponding joint. Second, a discrete computational model stimulates the continuous biological tissues. Finally, the connection lag time between neurons determines the intersegmental phase lag. The lag time function in the computational model is necessary.
Uses
Studying fish behavior Achieving a consistent response is a challenge in animal behavioral studies when live stimuli are used as independent variables. To overcome this challenge, robots can be used as consistent stimuli for testing hypotheses while avoiding large animal training and use. The controllable machines can be made to "look, sound, or even smell" like animals. We can obtain a better perception of animal behavior by turning to robot use in place of live animals because robots can produce a steady response in a set of repeatable actions. Moreover, with various field deployments and a greater degree of independence, robots hold the promise of assisting behavioral studies in the wild.
Toys
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