摘要
基于神经网络的连续状态空间Q学习已应用在机器人导航领域。针对神经网络易陷入局部极小,提出了将支持向量机与Q学习相结合的移动机器人导航方法。首先以研制的CASIA-I移动机器人和它的工作环境为实验平台,确定出Q学习的回报函数;然后利用支持向量机对Q学习的状态——动作对的Q值进行在线估计,同时,为了提高估计速度,引入滚动时间窗机制;最后对所提方法进行了实验,实验结果表明所提方法能够使机器人无碰撞的到达目的地。
Continuous Q-learning algorithm based on neural has been used in robotic navigation domain for its simplicity and well-developed theory.Aiming at the neural easily falling into local minimum,a new mobile robot navigation method using Q-learning based on a Support Vector Machine(SVM) is proposed.According to the developed mobile robot CASIA-I and its working environment,an approach is proposed,used to determine the reward/penalty function of Q-learning.A SVM is used to estimate the Q-value of state-action pair on-line,at the same time,in order to decrease the on-time learning time of SVM,a sliding time-window is introduced.Experimental results are included to show that the action policy obtained through Q-learning based on SVM can make the mobile robot reach the destination without obstacle collision.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第23期242-244,248,共4页
Computer Engineering and Applications
基金
河南省科学技术厅基础与前沿技术研究计划项目(No.102300410242)
河南省教育厅自然科学基金项目(No.2010A510009)
河南省科技厅科技发展计划项目(No.112300410210)
关键词
移动机器人
Q学习
支持向量机
导航
在线学习
mobile robot
Q-learning
support vector machine
navigation
on-line learning