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A new adaptive state space construction method for the mobile robot navigation |
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| Authors: | Huang Bingqiang Cao Guangyi Fei Yanqiong Li Jianhua |
| Affiliation: | 1. Department of Automation, Shanghai Jiaotong University, Shanghai 200030, P.R.China 2. Institute of Robotics Research, Shanghai Jiaotong University, Shanghai 200030, P.R.China 3. Department of Computer Science, East China University of Science and Technology, Shanghai 200237, P.R.China |
| Abstract: | 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. |
| Keywords: | reinforcement learning normalized radial basis function (NRBF) function approximation robot navigation |
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