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Learning is an important capability for an individual robot, which provides an effective way for understanding, planning, and decision-making in a complex environment. For robot motion control, a local weighted k-nearest neighbors states selection method based on environment information and task information is presented. Based on this method, TD reinforcement learning algorithm is combined to reduce...
The swimming backward method for biomimetic carangiform robotic fish is analyzed in this paper based on the dynamic/kinematic model. The equation of Lagrange of multi-link carangiform robotic fish and simplified fluid force are inducted to calculate the dynamic and kinematic characteristics of the motions. A specific gait is calculated to make the profile of the carangiform robotic fishpsilas undulation...
This paper proposes a kinematic modeling method for a bio-inspired robotic fish based on single joint. Lagrangian function of freely swimming robotic fish is built based on a simplified geometric model. In order to build the kinematic model, the fluid force acting on the robotic fish is divided into three parts: the pressure on links, the approach stream pressure and the frictional force. By solving...
In this paper, a networked control system for a biomimetic robot fish is introduced. The whole system includes four parts: a network camera which is embedded with FTP and Web server and used as a local sensor; a remote computer which works as a networked controller and obtains sensor information from the network camera, processes the information, generates instructions for the robot fish, and sends...
In this paper, a method for the posture control of a biomimetic robot fish TPF-I is proposed. In this method, the position of the robot fish's gravity centre can be changed by a barycenter-adjustor, which leads to the pitching angle changing. Propelled by coordinating a multi-link body and a tail, the robot fish can complete the posture control and 3-D Locomotion. The 3-D locomotion and posture control...
In this paper, we developed a robot fish consisting of a fish body, a two-linked fish tail and two 2-DOF pectoral fins. In order to make robot fish detect obstacles and perceive its postures, three infrared sensors and a gyro are fixed on the fish body. And an artificial CPG controller consists of four neural oscillators with sensory information feedback have been designed. This controller can generate...
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