文章基本信息

标题：CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot
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作者：Canglong Liu ; Bin Zheng ; Chunyang Wang 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2017
卷号：139
页码：1-4
DOI：10.1051/matecconf/201713900007
语种：English
出版社：EDP Sciences
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.
关键词：enEnd-to-End LearningCNN;Obstacle AvoidanceROS