Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots
Various mechanisms have recently been developed that combine linkage mechanisms and wheels. In particular, the combination of passive linkage mechanisms and small wheels is a main research trend because standard wheeled mobile mechanisms find it difficult to move on rough terrain. In our previous research, a six-wheel mobile robot employing a passive linkage mechanism has been developed to enhance maneuverability and was able to climb over a 0.20 m bump and stairs. We designed a hybrid velocity and torque controller using a neural network since simple velocity controllers fail to climb up. In this paper, we propose an environment recognition system for a wheeled mobile robot that consists of multiple classification analyses to make the robot more adaptive to various environments by selecting a suitable system such as decision making, navigation and controller using the result of the environment recognition system. We evaluate the recognition performance in operation environments; slopes, bumps and stairs by comparing principle component, k-means and self-organizing map analyses.
wheeled mobile robot neural network self-organizing map environment recognition
Atsushi Kanda Masanori Sato Kazuo Ishii
Department of Brain Inspired Science and Engineering, Kyushu Institute of Technology, Kitakyushu 808-0196, Japan
国际会议
第二届国际仿生工程会议(The International Conference of Bionic Engineering 2008)(ICBE08)
长春
英文
113-120
2008-10-10（万方平台首次上网日期，不代表论文的发表时间）