This paper addresses the design problem of reliable H∞ controller for linear systems with state quantization.B...
Xingang.Zhao1,Dan.Ye2 1.State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China2.College of Information Science and Engineering,Northeastern University,Shengyang 110004,China
＜正＞A new linear programming obstacle-avoided model is presented in target pursuit and obstacle-avoided problem...
Yang Liying~(1,2),Wu Chengdong~3,Han Jianda~1,Zhao Xingang~1 and NIE Yiyong~1 1.State Key Laboratory of Robotics,Shenyang Institute of Automation,CAS,Shenyang 110015 2.Graduate School,Chinese Academy of Sciences,Beijing 3.School of Information Science & Engineering,Northeast University,Shenyang 110004
Gaussian mixture algorithm （GMA） is an effective approach for off-road terrain estimation, but still suffers from some difficulties in practical applications, such as complex calculation and object abstraction. In this paper, GMA is modified to improve its real-time performance and to provide it with a potential ability of obstacle detection. First, a selection window is designed based on the dominant-ellipse-principle to limit the probability distribution area of each measurement point, therefore avoiding the calculation on the cells outside the dominant ellipse. Second, a clustering approach is proposed in order to distinguish objects efficiently and decrease the operation area of one laser scan. Third, a virtual point vector is introduced to further reduce the computational load of the mean square error matrix. The modified GMA is experimented on a tracked mobile robot, and its improved performance is shown in comparison to the original GMA.
