In order to search and rescue the victims in rubble effective/): a 3-0 mop of the rubble is required. As a part of the national project of rescue mbot qstem, we are inuestigating a method for constructing a 3-0 map of rubble by teleoperated mobile mbots. We are also planning to build an intuitive user interface for teleoperating robots and navigating in a virtualized rubble modelusing the obtained 3-0 model. In this paper; some preliminary research results are intmduced. We did some design studies of laser rangejnders rhat con be mounted on U mobile mbot ond can get the range datu of the rubble amund the mbot. Then, we formulated a 3 0 S U M (Simultaneous Localization and Map BuildingJ algorirhm and conducted some simulation studies. Lasrlx we pmposed a novel morion canceling camera system and cor@"d its validiry by experiment.

Motion planning for mobile robots considering occluded obstacles is a navigation challenge in dynamic environments. If an obstacle suddenly appears from the occluded area, a robot might collide with the obstacle. This is the occlusion problem. Therefore, this paper proposes a novel motion planner, Velocity Obstacle for Occlusion, VOO. The VOO is based on Velocity Obstacle, VO, which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, speed, and moving direction, are quantitatively addressed. Thus the robot based on the VOO is enabled to move in consideration of both observed and occluded obstacles. Through simulations and real robot experiments, we discuss the effectiveness of the VOO for the occlusion problem by comparing to the VO.

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

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