中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	CNN-based vision model for obstacle avoidance of mobile robot
	Liu, Canglong1,2; Zheng, Bin2; Wang, Chunyang1; Zhao, Yongting2; Fu, Shun2; Li, Haochen2
	2017
摘要	Exploration in a known or unknown environment for a mobile robot is an essential application. In the paper, we study the mobile robot obstacle avoidance problem in an indoor environment. We present an end-to-end learning model based Convolutional Neural Network (CNN), which takes the raw image obtained from camera as only input. And the method converts directly the raw pixels to steering commands including turn left, turn right and go straight. Training data was collected by a human remotely controlled mobile robot which was manipulated to explore in a structure environment without colliding into obstacles. Our neural network was trained under caffe framework and specific instructions are executed by the Robot Operating System (ROS). We analysis the effect of the datasets from different environments with some marks on training process and several real-time detect experiments were designed. The final test result shows that the accuracy can be improved by increase the marks in a structured environment and our model can get high accuracy on obstacle avoidance for mobile robots. © The Authors, published by EDP Sciences, 2017.
语种	英语
DOI	10.1051/matecconf/201713900007
会议(录)名称	3rd International Conference on Mechanical, Electronic and Information Technology Engineering, ICMITE 2017
收录类别	EI
会议地点	Chengdu, China
会议日期	December 16, 2017 - December 17, 2017