Robosoft (France)

Voxel inversion is a well-established method for constructing a physical property model from geophysical data. However, a limitation on Cartesian voxel inversion is that the voxel earth model is restricted to prism shaped elements which do not conform to geology and can lead to numerical artefacts. Octree and unstructured meshes have been used to overcome this limitation, but both add significantly to either the number of voxel elements, or to the complexity of the representation. We propose an alternative method which maintains much of the simplicity of the regular Cartesian voxel model while allowing very accurate geometric representation of geological surfaces: the Cartesian cut cell (CCC) method.A significant geologic surface in most voxel inversions is the topography, which is very poorly represented by voxels. There are many other common geologic sub- surfaces including faults, contacts, unconformities, mineralized zones, etc. We demonstrate the necessity of improving the conventional voxel representation to ensure accurate geophysical modelling and how this is achieved with the CCC method. We also show the value of the CCC method in constrained inversion. These examples demonstrate that the CCC method allows accurate representation of geologic surfaces with a minimal extension to the simplicity of a conventional voxel model.

In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.

The European research project INT-MANUS embedded in the I*PROMS European network of excellence addresses the increasing demand for flexibility and adaptivity, which is summarised by rapid reconfiguration of complete factories, flexible reaction to new demands as well as related aspects in human computer interaction (HCI), software, and production systems. The project's main goal has been to develop a new technology for the production plants of the future: the Smart Connected Control Platform (SCCP). This platform allows controlling a factory with the help of an open distributed and learning agent platform that integrates machines, robots, and human personnel. It offers an enterprise service bus like concept for dynamic and decentrally controlled production systems, which flexibly connects machines and IT systems like robotic transport systems, terminals, mobile control systems, etc.

This paper deals with design and implementation of a stabilization algorithm for a car like robot performing high speed turns. The control of such a kind of system is rather difficult because of the complexity of the physical wheel- soil interaction model. In this paper, it is planned to analyze the complex dynamic model of this process to elaborate a stabilization algorithm only based on the measurement of the system yaw rate. Finally, a 3D simulation is performed to evaluate the efficiency of this designed stabilization algorithm.

This paper presents the "robuCAB", a Cybercar developed by ROBOSOFT, and the results of experimentation in a public and pedestrian area. In a first part, the hardware and software architecture of the vehicle - control, safety and HMI - are presented. The results of experimentation are detailed and the perspectives of evolution (control, safety, HMI) are discussed.

Tomorrow, advanced mobile robotic applications have to be able to cope with various situations and perform tasks in a dynamic environment. Furthermore, finding concrete applications in a wide range of user oriented industrial products, such systems, embedding several computing units, have to support both increasing demand of interactivity and number of non-critical pieces of hardware and software. To this aim not only advanced programming techniques, but also appropriate control architectures are required. This paper proposes a generic methodology, actually in use for our own products, that offers both the possibility to model such internally competing systems and the capability to coordinate them. A special parallel is drawn between our approach and the SynDEx software methodology, developed by INRIA, on which our current implementation relies.

Tomorrow, advanced mobile robotic applications have to be able to cope with various situations and perform tasks in a dynamic environment. Furthermore, finding concrete applications in a wide range of user oriented industrial products, such systems, embedding several computing units, have to support both increasing demand of interactivity and number of non-critical pieces of hardware and software. To this aim not only advanced programming techniques, but also appropriate control architectures are required. This paper proposes a generic methodology, actually in use for our own products, that offers both the possibility to model such internally competing systems and the capability to coordinate them. A special parallel is drawn between our approach and the SynDEx1 software methodology, developed by INRIA, on which our current implementation relies.