Presents a methodology of environmental support for autonomous mobile robots using visual marks with memory storage. The mark makes up for insufficient function of sensing and recognition; self-positioning, positioning of objects, and deciding methods to operate objects. The mark proposed in this paper consists of a landmark part and a memory part. The landmark part is used to estimate the relative position and orientation between robots and the mark, and the memory part gives information about what it is, what tasks there are, and how to conduct the tasks. Task execution using the marks with real robots is described to show the effectiveness of the proposed methodology.

One of the major emerging problems of distributed mobile robotic system is what kind of inter-robot communication to use, because of increasing robots integrated in the system. This paper aims to give an analytical view of this issue. The efficiency of local and global communication is compared based on the analysis of information transmission time to multiple robots. We will show that local communication is effective in environments where cooperative tasks are executed by multiple mobile robots in distributed fashion.

When many mobile robots should achieve cooperation, a local communication system is considered appropriate from the standpoint of the cost and capacity of communication. This paper presents the optimization of the efficiency of local communication in environments where many mobile robots send out information stochastically. The optimal communication area is derived by minimizing the transmission waiting time calculated using the probability of successful information transmission. Computer simulations have been undertaken to verify the analytical results.

Local communication system is considered appropriate when many mobile robots should achieve cooperation, from the standpoint of cost and capacity of communication. This paper presents a design method of the optimal communication range for efficient local communication system. The analyses of optimization are made by minimizing the information transition time, first in the case of transmission to an arbitrary robot, and next, to multiple robots. Computer simulations have been undertaken to verify the analytical results.