Mobile platforms when negotiating steps and stairs should be able to control theirs posture in order to avoid sudden tilting or falls. In particular, when considering applications for search and rescue operations where users have a very limited time of operation, the motion on stairs should be automated as much as possible. In this way operators can concentrate on their tasks (i.e. search of survivors and/or exploration of dangerous environments) rather than having to focus on the stability of the vehicle. A simple but very effective mechanism called ldquotailrdquo is introduced. The mechanical design and its control method is presented together with several tests and experiments carried out with a simple tracked vehicle in real environments.

Fire brigades and special agencies are often demanded to operate for search and aid of human lives in extremely dangerous scenarios. It is very important to first verify the safety of the environment and to obtain remotely a clear image of the scenario inside buildings or underground spaces. Several studies have been addressing the possibility of using robotic tools to carry out safe operations. This contribution presents the development of the HELIOS team, consisting of five tracked robots for urban search and rescue. Two units are equipped with manipulators for the accomplishment of particular tasks, such as the handling of objects and opening doors; the other three units, equipped with cameras and laser range finders, are utilized to create virtual 3D maps of the explored environment. The three units can move autonomously while collecting the data by using a collaborative positioning system (CPS). After an overview on the specifications of the team of robots and with respect to previous publications, detailed information about the improvements of the robot mechanical design and control systems are introduced. Tests of the CPS system and HELIOS IX vehicle together with a typical mission experiment are presented and discussed.

In this paper, we propose a constrained least squares approach for stably computing Laplacian deformation with strict positional constraints. In the existing work on Laplacian deformation, strict positional constraints are described using large values of least squares weights, which often cause numerical problems when Laplacians are described using cotangent weights. In our method, we describe strict positional constraints as hard constraints. We solve the combination of hard and soft constraints by constructing a typical least squares matrix form using QR decomposition. In addition, our method can manage shape deformation under over-constraints, such as redundant and conflicting constraints. Our framework achieves excellent performance for interactive deformation of mesh models.

We propose an advanced cut-and-paste editing for three-dimensional models. We introduce a new parameterization technique based on constrained B-spline surface/volume fitting. Our cut-and-paste editing is performed on this parameterization. In our method, a parametric volume is generated so that its isoparametric surface fits to the base surface and the volume bounds the detail. Our volume fitting is realized by the optimization of an objective functional with a smoothness factor. We implemented and evaluated our method, and showed that our constrained fitting is effective to edit shapes that may have highly curved areas or handles.

The control polygons of B-spline subdivision curves are usually refined uniformly using a technique called knot-doubling. This uniform refinement approach would perform unnecessary subdivision steps on portions already close to the limit curve enough and, consequently, cause unnecessary (exponential) increase on the number of line segments in the refined polygons. This paper overcomes this problem by proposing a local refinement technique for the control polygons of B-spline subdivision curves. Local refinement is achieved by selectively inserting new knots at midpoints of knot intervals. Efficient adaptive subdivision can be easily realized based on the new technique.

Streamline modeling is a design methodology of fair free form surfaces where the tangent vectors are specified/manipulated to generate/deform them instead of the control points of the traditional surface representations (K.T. Miura et al., 1998). Creations and deformations of complex 3D free form shapes generally require a large amount of labour and cost. Therefore, the authors propose a novel modeling technique based on of fluid flow dynamics in order to create and deform high-quality surfaces more intuitively with a smaller number of parameters based on the streamline modeling. We construct a flow field based on the potential flow, then calculate tangent vectors in the flow field that are required for the streamline modeling. Streamlines are generated by numerically integrating the tangent vectors, then the surface is represented as a set of streamlines. The deformation of the surface is performed by changing of the flow field. We have developed a prototype design system using the new modeling technique.

Existing data embedding algorithms for polygonal meshes and their attributes cannot be applied to the majority of (geometric) computer aided design (CAD) applications for two major reasons. First, these CAD systems employ parametric curves and surfaces, not polygonal meshes, as their main shape-defining primitives. Second, most CAD applications do not tolerate modifications of model topology and/or geometry that are introduced by existing data embedding algorithms. We propose a new data embedding algorithm for non-uniform rational B-spline (NURBS) curves and surfaces, which employs rational linear reparameterization for embedding messages. The algorithm exactly preserves the shape, that is, the geometry and topology of its embedding targets. Furthermore, it preserves the data size of the model. We consider how these two properties, preservation of shape and preservation of data size, can be significant with regard to the use of data embedding in CAD applications. In addition to the shape- and data size-preserving data embedding algorithm for NURBS curves and surfaces, we outline additional methods for embedding data in various types of parametric curves and surfaces.

This paper discusses techniques for embedding data into three-dimensional (3-D) polygonal models of geometry. Given objects consisting of points, lines, (connected) polygons, or curved surfaces, the algorithms described in produce polygonal models with data embedded into either their vertex coordinates, their vertex topology (connectivity), or both. Such data embedding can be used, for example, for copyright notification, copyright protection, theft deterrence, and inventory of 3-D polygonal models. A description of the background and requirements is followed by a discussion of where, and by what fundamental methods, data can be embedded into 3-D polygonal models. The paper then presents several data embedding algorithms, with examples, based on these fundamental methods. By means of these algorithms and examples, we show that the embedding of data into 3-D polygonal models is a practicable technique.

The advantages of digital media such as the Internet and CD-ROMs lie in the fact that their contents are easy to duplicate, edit, and distribute.These advantages, however, are double-edged swords, because they also facilitate unauthorized use of such contents.Data embedding, which places information into the contents themselves, is an approach to address this issue.Embedded information can be used, for example, for copyright protection, theft deterrence, and inventory.This paper discusses our work on embedding data into three-dimensional (3D) polygonal models of geometry.Given objects consisting of points, lines, polygons, or curved surfaces, the data embedding algorithms described in this paper produce polygonal models with data embedded.Data are placed into 3D polygonal models by modifying either their vertex coordinates, their vertex topology (connectivity), or both.A brief review of related work and a description of the requirements of data embedding is followed by a discussion of where, and by what fundamental methods, data can be embedded into 3D polygonal models.The paper then presents data-embedding algorithms, with examples, based on these fundamental methods.