Abstract
The aim of this paper is to investigate a control framework for mobile robots, operating
in shared environment with humans. The Intelligent Space (iSpace) can sense the whole space and
evaluate the situations in the space by distributing sensors. The mobile agents serve the inhabitants
in the space utilizes the evaluated information by iSpace. The iSpace evaluates the situations
in the space and learns the walking behavior of the inhabitants. The human intelligence manifests
in the space as a behavior, as a response to the situation in the space. The iSpace learns the behavior
and applies to mobile agent motion planning and control. This paper introduces the application
of fuzzy-neural network to describe the obstacle avoidance behavior learned from humans.
Simulation results are introduced to demonstrate the efficiency of this method.
