Environment recognition system based on multiple classification analyses for mobile robots

Various mobile mechanisms have been developed combining linkage mechanisms and wheels, and the combination of passive linkage mechanisms and small wheels is one of research trends to enhance the mobility on irregular terrain. We have been working on a 6-wheeled mobile robot employing a passive linkage mechanism, which achieved climbing capability over a 0.20[m] height of bump and stairs, and developed velocity controllers using PID and neural network. In this paper, we propose an environment recognition system for the wheeled mobile robot, where multiple classification analyses such as Self-Organizing Map, k-means method and Principle Component Analyses are introduced and used for clustering robot's environments based on state variables such as joint angles and velocities of links, and attitude angles of the robot body. We evaluate the recognition performance through experiments.