文献类型：期刊文献

英文题名：New adaptive state space construction method for the mobile robot navigation

作者：Huang, Bingqiang[1]; Cao, Guangyi[1]; Fei, Yanqiong[2]; Li, Jianhua[3]

机构：[1] Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China; [2] Institute of Robotics Research, Shanghai Jiaotong University, Shanghai 200030, China; [3] Department of Computer Science, East China University of Science and Technology, Shanghai 200237, China

年份：2008

卷号：14

期号：2

起止页码：182

外文期刊名：High Technology Letters

收录：EI(收录号：20082611336302)

语种：英文

外文关键词：Approximation theory - Functions - Navigation systems - Radial basis function networks - Reinforcement learning - State space methods

摘要：In order to solve the combinative explosion problems in a continuous and high dimensional state space, a function approximation approach is usually used to represent the state space. The normalized radial basis function (NRBF) was adopted as the local function approximator and a kind of adaptive state space construction strategy based on the NRBF (ASC-NRBF) was proposed, which enables the system to allocate appropriate number and size of the basis functions automatically. Combined with the reinforcement learning method, the proposed ASC-NRBF method was applied to the robot navigation problem. Simulation results illustrate the performance of the proposed method.