Autonomous robots in rescue environments have to occupied. To generate this map, the algorithm uses the robot's fulfill several task at the same time: They have to localize themselves, odometry data and the sensor readings of a Hokuyo URGbuild maps, detect victims and decide where to go next for further 04LX laser range finder. The map building process uses a parexploration. In this contribution we present an approach that provides a robust solution for the exploration task: Based on the knowledge Tid fitroach the centfscantosthe occupancy Rid. of the environment that the robot has already acquired, the algorithm This approach was successfully tested during the RoboCup calculates a path to the next interesting "frontier". Our comprehensive World Championship 2006, but relied on a remotely controlled approach takes into account the distance to the next frontier and robot. To make the system fully autonomous, the exploration the difficulty of the path for the robot. Those difficulties can result behavior described in the following sections was implemented. from narrow passages but also from wide, open spaces where the sensors cannot detect any landmark. For the native exploration task The paper is organized as follows: In secion II, the related the algorithm is fed with occupancy grids. For the search task, it can work is presented. The different approaches for exploration also process maps that encode additional information, e. g. places that and path planning are explained. In section III the Exploration have not been searched by other sensors yet.

Three-dimensional laser range finders provide autonomous systems with vast amounts of information. However, autonomous robots navigating in unstructured environments are usually not interested in every geometric detail of their surroundings. Instead, they require realtime information about the location of obstacles and the condition of drivable areas.In this paper, we first present grid-based algorithms for classifying regions as either drivable or not. In a subsequent step, drivable regions are further examined using a novel algorithm which determines the local terrain roughness. This information can be used by a path planning algorithm to decide whether to prefer a rough, muddy area, or a plain street, which would not be possible using binary drivability information only.

The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (e.g. Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.ELLIITSHERP

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