This paper considers the problem of deploying a mobile sensor network in an unknown environment. A mobile sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self-deployment; i.e., starting from some compact initial configuration, the nodes in the network can spread out such that the area `covered' by the network is maximized. In this paper, we present a potential-field-based approach to deployment. The fields are constructed such that each node is repelled by both obstacles and by other nodes, thereby forcing the network to spread itself throughout the environment. The approach is both distributed and scalable.

This paper describes an incremental deployment algorithm for mobile sensor networks. A mobile sensor network is a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The algorithm deploys nodes one-at-atime into an unknown environment, with each node making use of information gathered by previously deployed nodes to determine its target location. The algorithm is designed to maximize network `coverage' whilst simultaneously ensuring that nodes retain line-of-sight with one another (this latter constraint arises from the need to localize the nodes; in our previous work on mesh-based localization [12, 13] we have shown how nodes can localize themselves in a completely unknown environment by using other nodes as landmarks). This paper describes the incremental deployment algorithm and presents the results of an extensive series of simulation experiments. These experiments serve to both validate the algorithm and illuminate its empirical properties.

A way to significantly advance robotic science is to perform experiments that can be replicated by other researchers and be used to compare different methods. This happens rarely in current robotics research. In this paper we present a methodology for performing experimental activities in the area of robotic mapping. The proposed methodology prescribes a number of issues that should be addressed when experimentally validating a mapping method. We present the application of the proposed methodology to a mapping system we have developed.

Abstract—This paper describes an algorithm for deploying a mobile sensor network. A mobile sensor network is made up of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. In this paper, we describe an incremental deployment algorithm in which nodes are deployed one-at-a-time into an unknown environment. Each node makes use of information gathered by previously deployed nodes to determine its optimal deployment location. The algorithm is designed to maximize network ‘coverage’ whilst ensuring that nodes retain line-of-sight with one another (this latter constraint arises from the need to localize the nodes: in our previous work on mesh-based localization [9], [10] we have shown how nodes can localize themselves in a completely unknown environment by using other nodes as landmarks). In this paper, we describe a series of experiments (conducted in both simulation and reality) aimed at validating the algorithm and illuminating its empirical properties.

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...

Some thoughts on the use of imprecision in intelligent systems are presented. Examples from knowledge-based systems, robotics and manufacturing are used to illustrate several methods that artificial intelligence proposes in order to model imprecision.