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This paper presents a novel approach to multi-robot site exploration and map building considering the robot team as a self-organising system. The approach has been developed within the framework of the project GUARDIANS. The Map Building process represents not a separate activity, but an inherent by-product of self-organisation. The system consists of an (heterogeneous) robot swarm, a mobile ad-hoc network and an (evolving) topological map of the environment. The proposed map building approach takes advantage of a cooperating robot team (as opposed to a single robot) allowing accurate deployment and localisation in a structured, yet dynamic manner. A topological graph representation of the environment is formed, from which an initial metric representation is elicitable as edges are assigned lengths. This reasonable sketch of the environment can be further developed to a full metric map and be used as the basis of building ad-hoc mobile wireless communication and sensor networks. The presented algorithms also take into consideration sensor limitation and are tested on a group of Khepera III robots, specially upgraded to fulfil the needs of our approach. [ABSTRACT FROM AUTHOR]

The Guardians multi-robot team is being designed to be deployed in a large warehouse to assist firefighters in the event or danger of a fire. The large size of the environment together with development of dense smoke that drastically reduces visibility, represent major challenges in search and rescue operations. The GUARDIANS robots act alongside a firefighter and should provide, among others, the following tasks: to guide or accompany the firefighters on the site while indicating possible obstacles and locations of danger and maintaining communications links. In order to fulfill the aforementioned tasks the robots need to be able to exert certain behaviours. Among the basic behaviours are capabilities to unite in a group - generate a formation - and navigate on the site while keeping this formation. The basic control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to fulfill specific tasks of the Guardians scenario. All of the tasks can be achieved without central control, and some of the tasks can be performed even without explicit communication among the robots. We discuss advantages and shortcomings of our model and present samples of implementation on ERA-MOBI robots, commonly referred to as Erratics. [ABSTRACT FROM AUTHOR]

We design a set of algorithms to construct and visualise unambiguous Gauss maps for a large class of triangulated polyhedral surfaces, including surfaces of non-convex objects and even non-manifold surfaces. The resulting Gauss map describes the surface by distinguishing its domains of positive and negative curvature, referred to as curvature domains. These domains are often only implicitly present in a polyhedral surface and cannot be revealed by means of the angle deficit. We call the collection of curvature domains of a surface the Gauss map signature. Using the concept of the Gauss map signature, we highlight why the angle deficit is sufficient neither to estimate the Gaussian curvature of the underlying smooth surface nor to capture the curvature information of a polyhedral surface. The Gauss map signature provides shape recognition and curvature characterisation of a triangle mesh and can be used further for optimising the mesh or for developing subdivision schemes. [ABSTRACT FROM AUTHOR]