A new adaptive state space construction method for the mobile robot navigation  

作　　者：黄炳强 Cao Guangyi Fei Yanqiong Li Jianhua 

机构地区：[1]Department of Automation, Shanghai Jiaotong University, Shanghai 200030, P.R. China] [2]Institute of Robotics Research, Shanghai Jiaotong University, Shanghai 200030, P.R. Chinal [3]Department of Computer Science, East China University of Science and Technology, Shanghai 200237, P.R. China

出　　处：《High Technology Letters》2008年第2期182-186,共5页高技术通讯（英文版）

基　　金：the National Natural Science Foundation of China(No50305021)

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

关 键 词：reinforcement learning normalized radial basis function （NRBF） function approximation robot navigation 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]