摘要
为了在传统场景分类器基础上进一步提高场景识别准确率,提出了一种采用Q学习(Q-learning)实现室内场景主动识别的算法.该算法采用极限学习机(Extreme Learning Machine,ELM)与反向传播梯度下降相结合的方式近似Q-learning值函数的神经网络.算法基于Q-learning动态地学习场景识别率最高的机器人朝向角,使机器人能够自主获取多次更为可靠的传感器信息并将对应识别结果融合,进而提高场景识别准确率.将算法应用在移动机器人场景识别中进行实验,结果表明该算法可以有效提高场景识别准确率.
In order to further improve the accuracy of scene recognition on the basis of the traditional scene classifier,an algorithm for automatic recognition of indoor scene using Q-learning is proposed.The algorithm fits the neural network of the Q-learning value function by the way of combining Extreme Learning Machine(ELM)and back propagation gradient descent.The algorithm can learn the robot orientation with the highest scene recognition rate dynamically based on Q-learning,which makes the robot acquire reliable sensor information several times autonomously and fuse corresponding recognition results,thus improving the accuracy of scene recognition.The algorithm is applied to the experiment of mobile robot scene recognition,and the result shows that this algorithm can effectively improve the accuracy of scene recognition.
作者
柳杨
王博文
韩建晖
孙英
LIU Yang;WANG Bowen;HAN Jianhui;SUN Ying(College ofElectric Engineering, Hebei University ofTechnology, Tianjin 300132, China)
出处
《河北工业大学学报》
CAS
2018年第1期8-18,共11页
Journal of Hebei University of Technology
基金
河北省自然科学基金(E2017202035)
河北省科技计划项目(16211709)
江西省精密驱动与控制重点实验室开放基金
关键词
场景识别
Q学习
极限学习机
反向传播梯度下降
机器人
传感器
scene recognition
Q-learning
extreme learning machine
back propagation gradient descent
robot
sensor