This paper presents a novel technique for analyzing and designing local communication systems for distributed mobile robotic systems (DMRS). Our goal is to provide an analysis-base guideline for designing local communication systems to efficiently transmit task information to the appropriate robots. In this paper, we propose a layered methodology, i. e. , design from spatial and temporal aspects based on analysis of information diffusion by local communication between robots. The task environment is classified so that each analysis and design is applied in a systematic way. The spatial design gives the optimal communication area for minimizing transmission time for various cooperative tasks. In the temporal design, we derive the information announcing time to avoid excessive information diffusion. The designed local communication is evaluated in comparison with global communication. Finally, we performed simulations and experiments to demonstrate that the analysis and design technique is effective for constructing an efficient local communication system.

Recently, various systems based on agent model architecture have been developed. In these systems, 'agents' with their own goals and functions are embedded, and perform their own tasks through collaboration among them by communication to achieve a goal as the system requires. Using this agent model for the construction of educational systems, adaptive configuration of the system is achieved. The purpose of this study is to propose a methodology for the design of an educational system based on agent model architecture. This paper describes the configuration of the agent model and the communication language and protocol used to represent collaboration among the agents necessary for performing a cooperative task. Moreover, we explain how to organize these agents as an educational system. As a case to show the organization of agents, we discuss the configuration of an intelligent learning environment to support C shell programming in UNIX and explain the collaborative behavior of embedded agents.