Kalman filters have been widely used for navigation It estimates the state of a dynamic system using two different in mobile robotics. One of the key problems associated with Kalman models, namely the dynamic and the observation model. The filter is how to assign suitable statistical properties to both the dynamic model describes the behavior of the state vector dynamic and the observational models. For GPS-based localization dnic oel describe h e l behavioreo the stlate vecor of a rough-terrain mobile robot, the maneuver of the vehicle and the whil teoservanmoe stabe th r ons be level of measurement noise are environmental dependent, and hard to tween measurements and the state vector. Both models are be predicted. This is particularly true when the vehicle experiences associated with statistical properties to describe the accuracy a sudden change of its state, which is typical on rugged terrain of the models. For many applications, the statistic noise due, for example, to an obstacle or slippery slopes. Therefore to levels of the model are given before the filtering process and assign constant noise levels for such applications is not realistic.In this work we propose a real-time adaptive algorithm for GPS wilmanltai a duringtthe whole r siveproces data processing based on the observation of residuals. Large value Commonly, this a priori statistical information is determined of residuals suggests poor performance of the filter that can be by test analysis and certain knowledge about the observation improved giving more weight to the measurements provided by the type beforehand. If such a priori information is inadequate GPS using a fading memory factor. For a finer gradation of this to represent the real statistic noise levels, Kalman estimation parameter, we used a fuzzy logic inference system implementing our ... 'physical understanding of the phenomenon. The proposed approach is not optimal and may produce unreliable results, somehmes was validated in experimental trials comparing the performance even leads to filtering divergence [4]. Such is the case when of the adaptive algorithm with a conventional Kalman filter for the vehicle comes to a sudden stop or steers with small vehicle localization. The results demonstrate that the novel adaptive turning radius. Those manoeuvres are hardly predictable by algorithm is much robust to the sudden changes of vehicle motion the filter. Therefore, a system with constant noise variances and measurement errors.is inadequate to satisfy all situations and difficult to design.

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Abstract-In this paper the authors present the current progress of a networked robotic system intented to be deployed at disaster areas. This system is formed by three mobile robots: two twin crawlers that will have search-and-recognition tasks gathering information about their surroundings, and an outdoorwheeled rover that will approach the area and which will also carry the two crawlers. The communication system for these robots consists of a wireless local area network that will have an operator located at a safe distance controlling them. As a final communication objective, the integration of a satellite-based IP communication, linked to the japanese satellite ETS-VIII is scheduled. In order to be able to navigate the crawlers remotely, a wireless LAN camera and a Laser Range Finder (LRF) sensor have been mounted on both of the crawlers. These LRFs will scan the area where the crawler is at, obtaining a detailed point-based 3D image. A special focus of this paper is made on the possibility of maneuvering the crawlers based only on the remotely-acquired LRF and camera's information.

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