In this paper, we introduce a road condition sharing system using Vehicle-to-Vehicle communication in various communication environment. Japan is prone to natural disasters. In addition, traffic hazards and traffic accidents occur due to snow. A wide range and quick condition understanding and monitoring are needed. In addition, it is important to understand the condition of the destination and the road condition to the destination. But, especially after a disaster has occurred, areas that mass media can provide disaster condition information are limited. Recently, various higher technologies have been developed and noticed. By using those technologies, we develop our system. Using our system, we can realize road condition understanding, monitoring and recording as an alert information using multiple sensor data, information sharing between each vehicles and information is provided as web application. As a quantitative evaluation, we measure vehicle to-vehicle communication quality.

To measure an absolute position of a mobile robot by external observation, many methods using vision system have been proposed. In these methods, a camera is mounted on a ceiling, etc., and a robot is detected by analyzing captured images from the camera. However, to construct distributed sensing system which is flexible for various environments, it becomes important to develop simpler observation device. In this paper, we propose new position measurement system for a mobile robot based on an incident angle detection of infrared light. Infrared light sources are mounted on a robot as markers. An observation device mounted on a ceiling measures only incident angles of infrared light from markers and calculates positions of the robot. After a procedure of geometrical calculation concerned with a position and an orientation of a robot based on measured incident angles, implementations of devices to transmit and receive infrared light are described. Finally, a performance of proposed method is shown through experiments concerned with position and orientation measurements.

We have proposed the collision avoidance methods in a multirobot system based on the information exchanged by the "LOCISS: Locally Communicable Infrared Sensory System", which is developed by the authors. One of the problems in the LOCISS based methods is that the number of situations which should be considered increases very much when the number of the robots and stationary obstacles in the working environment increases. In order to reduce the required computational power and memory capacity for such a large number of situations, we propose, in this paper, a multilayered reinforcement learning scheme to acquire appropriate collision avoidance behaviors. The feasibility and the performance of the proposed scheme is examined through the experiment using actual mobile robots.

In this paper, we discuss autonomous navigation in multirobot environment based on local communication. To carry out cooperative tasks using multiple autonomous robots, navigation systems should be implemented to the robots because mutual alignment of their positions and collision avoidance to each other become important. Therefore, it seems a collision avoidance and a self-localization are indispensable as elemental techniques for the navigation. Although these techniques need to recognize situation of robots surrounding environment, conventional devices to acquire information around them can not work sufficiently in such environment. For this problem, we propose techniques based on the local communication and the navigation system by combining them for multirobot environment. It is shown that robots can arrive their goal precisely without collision among them through proposed navigation systems.

We discuss adaptive behavior acquisition for collision avoidance among multiple autonomous mobile robots which are equipped with the locally communicable infrared sensory system (LOCISS). The LOCISS is a local sensing device for collision avoidance by which robots can detect other robots and obstacles and discriminate them by exchanging relevant information. We (1996) reported previously a collision avoidance method between two robots based on the predetermined rules using LOCISS. It is, however, difficult to realize collision avoidance among three or more robots by the predetermined rules only because situations around the robots become more complicated as the number of robots increases. Thus, it is desirable for the robots to have an adaptive capability for acquisition of the behaviors to avoid collision with other robots and obstacles. To acquire the adaptive behavior, the reinforcement learning is introduced in this paper. It is shown that appropriate behaviors for collision avoidance can be successfully acquired through the proposed learning process.

Collision avoidance is an essential problem for applications of multiple mobile robots. The authors have proposed new sensor system called LOCISS (Locally Communicable Infrared Sensory System) to detect robots and obstacles using infrared local communication. Robots mounting LOCISS can exchange useful information for collision avoidance such as speed and moving direction. In this paper, a set of rules for collision avoidance among multiple autonomous mobile robots using LOCISS is proposed. Each robot mutually carries out collision avoidance using the proposed rule. The validity of this set of rules is shown through a series of experiments using mobile robots.