Rough Set based Unstructured Road Detection through Feature Learning
This paper addresses the navigation problem of robot vehicle,Patrol-security Robot,on unstructured roads with degraded surfaces and edges,strong shadows and no lane markings.These conditions cause many road-following systems failed because the road feature extraction is not easy and the detection effect becomes inaccuracy.For solving the problem of the feature extraction of the complex unstructured road detection,a rough set based unstructured road detection (RSURD)method is presented.By learning from the samples of road images,the knowledge of road features is acquire and consummated gradually by on-line learning,and the accuracy of rules is improved,which make the system fit different road conditions.This method has been implemented and tested on Patrol-security Robot.Good adaptability,robustness and reliability have been accomplished.
Rough set Outdoor Mobile robot Unstructured road detection Feature extraction Patrol-security robot
Qingji Gao Qijun Luo Sun Moli
Nanjing University of Aeronautics and Astronautics ADD:29 Yudao St.,Nanjing 210016,China;Robotics In Robotics,Institute Civil Aviation University of China Xunhai Road,Dongli,Tianjin,China Department of Automation Northeast Dianlli University Changchun Road,Jilin city,Jinlin province,Chin
国际会议
2007 IEEE International Conference on Automation and Lofistics
山东济南
英文
2007-08-18（万方平台首次上网日期，不代表论文的发表时间）