This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem. A comprehensive urban disaster simulator is constructed on distributed computers. Heterogeneous intelligent agents such as fire fighters, victims and volunteers conduct search and rescue activities in this virtual disaster world. A real world interface integrates various sensor systems and controllers of infrastructures in the real cities with the virtual world. Real-time simulation is synchronized with actual disasters, computing complex relationship between various damage factors and agent behaviors. A mission-critical man-machine interface provides portability and robustness of disaster mitigation centers, and augmented-reality interfaces for rescue parties in real disasters. It also provides a virtual reality training function for the public. This diverse spectrum of RoboCup-Rescue contributes to the creation of the safer social system.

We report the result on RoboCup-97. RoboCup97 is the first RoboCup [1] competition which is held at IJCAI-97. RoboCup-97 consists on three leagues: simulation league. small-size real robot league and middlesize real robot league. The simulator (called Soccer Server) (see Fig.1) is designed and implemented by Noda[2]. The field of small-size league is a ping pong table. The field of middle-size league is 9 (3× 3) ping pong tables. The lists of the participants are as follows.

MIRACLE-IV is discussed, which is capable of obtaining an internal structure of an object from a series of silhouette images with no initial explicit models about the object. The images are derived from only one object, but the form is varied. The system is composed of two subsystems: a model acquisition part (the modeler) and an image processing strategy part (the strategist). On the assumption that the object consists of hinges, slides, and solids, the modeler learns their number in the object and the relationship among them. The strategist binds the functional features as hinges or slides with visual features in the actual image data. The image processing sequence for the extraction of the visual feature is not given but is learned automatically through trial and error.>