This paper introduces Robomote, a robotic solution developed to explore problems in large-scale distributed robotics and sensor networks. The design explicitly aims at enabling research in sensor networking, adhoc networking, massively distributed robotics, and extended longevity. The platform must meet many demanding criteria not limited to but including: miniature size, low power, low cost, simple fabrication, and a sensor/actuator suite that facilitates navigation and localization. We argue that a robot test bed such as Robomote is necessary for practical research with large networks of mobile robots. Further, we present a preliminary analysis of Robomotes' success to this end.

INTRODUCTION Advanced sensorimotor devices, like mobile robots, are often referred to as autonomous systems. The expression is used to intentionally remark on the difference between these systems and those of traditional industrial automation. Although no rigorous definition is easily available, a general informal consensus seems to exist on the features that denote autonomy: a system is considered the more autonomous the more reliably it can survive and perform tasks in the real world, without the need for human intervention. 171 ____________________________ *e-mail: claudio.sossai@isib.cnr.it J. of Mult.-Valued Logic & Soft Computing., Vol. 9, pp. 171-194 2003 Old City Publishing, Inc. Reprints available directly from the publisher Published by license under the OCP Science imprint, Photocopying permitted by license only a member of the Old City Publishing Group In the literature different ideas and techniques have been proposed and investigated to achieve these results, nonethe

The interpretation of data coming from the real world may require different and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. As it properly extends classical logic, it also allows the fusion of data with different semantics and symbolic knowledge. The approach has been applied to the problem of mobile robot localization. For each place in the environment, a set of logical propositions allows the system to calculate the belief of the robot's presence as a function of the partial evidences provided by the individual sensors.

A mobile robot localization system based on sensor fusion is described. Data coming from various sensors can require di#erent and often complementary uncertainty models: some are better described by possibility theory, others are intrinsically probabilistic. A logic for belief functions is introduced to axiomatize both semantics as special cases. For each place in a map of the environment, a set of logical rules allows to calculate the belief of the robot's presence, as a function of the partial evidences provided by the individual sensors. Various experimental runs have shown promising results. 1 Introduction The aim of this work is to apply to robotics a logical framework where di#erent uncertainty models, like belief functions, possibilities (i.e. consonant belief functions) and probabilities (i.e. additive belief functions) can be uniformly axiomatized. 1.1 Uncertainty models Although probability theory has a leading position in the description of uncertainty, in th...