![]() Second, a discrete computational model stimulates the continuous biological tissues. Designers can implement one CPG in each segment to control the corresponding joint. First, the bionic propeller adopts one servomotor to drive a joint while the fish has two group muscles in each joint. There are several key points in the design of bionic neural networks. Similar to their role in living fish, neural networks are used to control robot fish. After the systems form a steady locomotion, the signal from the cerebrum stops and the CPGs can produce and modulate locomotion patterns. The cerebrum, the most anterior part of the brain in vertebrates, can control signal inputs to startup, stop and turn. The CPG is located in every segment, and can connect and stimulate contracting or stretching muscles. Ī central neural system known as a " Central Pattern Generator" (CPGs) can govern multilink robotic fish locomotion. Ensuring swimming stability gait can be difficult, and transitioning smoothly between two different gaits can be tricky in robot fish. This kind of body wave-based swimming control should be discrete and parameterized for a specific swimming gait. 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. Designers should consider some important factors, including lateral body motions, kinematic data and anatomical data. The key to controlling a multi-joint robotic fish is creating a simplified mechanism that is able to generate a reasonable amount of control. The propulsive performance is related to the position, mobility, and hydrodynamic characteristics of the control surfaces. In order for robot fish to achieve the same type of rapid and maneuverable propulsion, robot fish need multiple control surfaces. 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. By including pectoral fins, robot fish can perform force vectoring and perform complex swimming behaviors instead of forward swimming only. A diverse option of fins can be used in the creation of robot fish to achieve this goal. Realistic Propulsion Systems can help improve autonomous maneuvering and exhibit a higher level of locomotion performance. This simple formula is used when calculating the locomotion of both robot and living fish. The mean thrust can be calculated entirely from the displacement and swimming speed at the trailing edge of the caudal fin. 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. Slender-body theory is often used when studying robot fish locomotion. ![]() Thus, researchers have focused on tail kinematics when developing robot fish motion. ![]() Living fish have powerful muscles that can generate lateral movements for locomotion while the head remains in a relatively motionless state. The posterior tail creates thrust force, making it one of the most important parts of the robot fish. Some studies show this kind of tail shape increases swimming speeds and creates a high-efficiency robot fish. One of the many tail shapes found on robot fish is lunate, or crescent shaped. In order to control and analyze robotic fish movement, researchers study the shape, dynamic model and lateral movements of the robotic tail. 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. The first robot fish (MIT's RoboTuna) was designed to mimic the structure and dynamic properties of a Tuna. 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. For example, designers attempt to create robots with flexible bodies (like real fish) that can exhibit undulatory motion. Engineers often focus on functional design. ![]()
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