Abstract: A wheeled mobile mechanism with a passive and/or active linkage mechanism for rough terrain environment is developed and evaluated. The wheeled mobile mechanism which has high mobility in rough terrain needs sophisticated system to adapt various environments.
We focus on the development of a switching controller system for wheeled mobile robots in rough terrain. This system consists of two sub-systems: an environment recognition system using link angles and an adaptive control system. In the en-vironment recognition system, we introduce a Self-Organizing Map (SOM) for clustering link angles. In the adaptive control-lers, we introduce neural networks to calculate the inverse model of the wheeled mobile robot.
The environment recognition system can recognize the environment in which the robot travels, and the adjustable con-trollers are tuned by experimental results for each environment. The dual sub-system switching controller system is experi-mentally evaluated. The system recognizes its environment and adapts by switching the adjustable controllers. This system demonstrates superior performance to a well-tuned single PID controller.

Key words: mobile robot, rough terrain, neural network, Self-Organizing Map