Fuzzy neural network based mobile agent control for intelligent space

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 serving the inhabitants in the space utilizes the evaluated information by iSpace. This paper focuses on the motion planning and control of mobile agent. The intelligent behavior, like obstacle avoidance and activity planning is learned from inhabitants. The iSpace evaluates the situations in the space and learns the walking behavior of the inhabitants. Two items are discussed in this paper. One is to propose a control framework for mobile agents in iSpace, what is based on human behavior model. 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. The other is to propose an application of mathematical model to realize the behavior learning and application. 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.