Application of Reinforcement Learning to Autonomous Heading Control for Bionic Underwater Robots
The bionic underwater robot propelled by undulating fins is an interesting field in current research on underwater robots. With the prosperous development of bionic underwater robots, its control problem remains big challenging for strong nonlinearity, uncertainty environments, and lack of understanding of dynamic characteristics of undulating fins. As a model-free method, the Q-learning based Reinforcement Learning achieves its control motivation by interacting with the environment and maximizing a reward, so suits the complicated applications such as robot control. This paper introduced the online Q_learning algorithm to the autonomous heading control for a kind of bionic underwater robot with two undulating fins. The algorithm doesnt need to know any knowledge about the robot, and can learn the internal mapping between states and actions that control behaviors must contain. With the simulation experiments, the validity of Reinforcement Learning algorithm in autonomous heading control of the bionic underwater robot was validated.
Longxin Lin Haibin Xie Lincheng Shen
National University of Defense Technology,Changsha,China
国际会议
2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)
桂林
英文
2486-2490
2009-12-19（万方平台首次上网日期，不代表论文的发表时间）