摘要
分析了井下钻孔机器人避障中超声波传感器的局限性,并提出解决方案.着重指出对超声波进行温、湿度补偿,尝试用E lman反馈神经网络逼近函数.E lman网络隐层采用“tansig”激活函数,输出层用“pureline”激活函数,保证了只要有足够多的隐层神经元个数,网络就可以任意精度逼近任意函数.经实验验证,对超声测距进行温、湿度补偿后,其测量精度提高了两个数量级,大大改善了系统中避障模块的工作效率,提高了钻孔机器人躲避障碍物的能力.
Analyzed the limitation of the ultrasonic sensor in the underground drilling robot avoids obstacle, put forward the scheme solved. To emphasize that, carry on warm and humidity compensation to ultrasonic wave sensor. Try to use Elman feedback neural network to approach function. Elman network latent layer adopt ＂ tansig ＂ activate function, output layer activate function with ＂ pureline ＂, this guarantee that once the network has enough layers, it can approach wanton function with wanton precision. Proved by experiment that ultrasonic ranging＇s measure precision raise two orders of magnitude after carried on the warm and humidity compensate, improved the working efficiency of obstacle avoidance in the system greatly, and improved the ability of drilling robot＇s obstacle avoidance.
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2005年第6期783-787,共5页
Journal of China Coal Society
基金
辽宁省教育厅基金项目(2004C011)
关键词
超声测距
井下机器人
避障
Ultrasonic ranging
pit robot
obstacle avoidance
