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Since 1995 autonomous, intelligent or mobile robot systems have been investigated and included in the education process. Using commercial and/or open source platforms like LEGO-techniques with the 6.270-Board (Flynn, A.M. and Jones, J.L., 1996), the EyeBot controller (Braunl, Th., 2002) or the PIONEER robots various aspects like mobility, controllability, sensor fusion, line or object recognition, navigation and control strategies were taken into account. The limited abilities of all these systems in computing power or image processing required the development of a new hardware platform called RCUBE (Boersch, I., et al., 2003) combining the features of a typical actuator-sensor-input-output-board with computing power and image recognition capabilities. The system would be suitable as a compact platform for universities, small enterprises and private developers.
Since 1995 mobile robot systems have been
investigated and included in the education process.
Using commercial and/or open source platforms like
LEGO-techniques with the 6.270-Board [5], the
EyeBot controller [3] or the PIONEER robots [10]
various aspects of mobility, sensing, control and
navigation were taken into account. The limited
abilities of all these platforms in computing power or
image processing required the development of a new
hardware platform called RCUBE [2], [11] combining
the features of a typical actuator-sensor-input-outputboard
with computing power and image recognition
capabilities. The system is suitable as a compact
platform for universities, small enterprises and private
developers.
An intelligent robot platform for autonomous systems with vision capabilities has been developed by the University of Applied Sciences Brandenburg in cooperation with SME's. The system is suitable as a research and education platform for universities, a basis for industrial applications and for private developers of robots. This paper presents an architecture overview and application details of the RCUBE system. RCUBE is available in 2 modular versions: (1) a cost-effective platform for reactive robots and private developers (2) a performant platform for intelligent robots with image processing capabilities suitable for research, development and education in the field of service robotics. The system consists of 3 ready-to-program hardware modules with a basic software layer. They can be combined in different configurations. The modularity provides the flexibility to configure an RCUBE system for different application areas, but also single modules can be used in applications such as autonomous intelligent cameras or planning robots (e.g. lawnmower, vacuum cleaner). A key feature is the capability to autonomous image processing combined with sensors and actors, small size and low power consumption. RCUBE opens the way to small independent seeing robots.
Wissensverarbeitung : eine Einführung in die Künstliche Intelligenz für Informatiker und Ingenieure
(2007)
Resistance spot welding is the dominant process in the present mass production of steel constructions without sealing requirements with single sheet thicknesses up to 3 mm. Two of the main applications of resistance spot welding are the automobile and the railway vehicle manufacturing industry. The majority of these connections has safety-related character and therefore they must not fall below a certain weld diameter. Since resistance spot welding has been established, this weld diameter has been usually used as the gold standard. Despite intensive efforts, there has not been found yet a reliable method to detect this connection quality non-destructively. Considerable amounts of money and steel sheets are wasted on making sure that the process does not result in faulty joints. The indication of the weld diameter by in-process monitoring in a reliable way would allow the quality documentation of joints during the welding process and additionally lead through demand-actuated milling cycles to a substantial decrease of electrode consumption. An annual, estimated reduction in the seven- to nine-figure range could be achieved. It has an important impact, because the economics of the process is essentially characterized by the electrode caps (Klages 24). We propose a simple and straightforward approach using data mining techniques to accurately predict the weld diameter from recorded data during the welding process. In this paper, we describe the methods used during data preprocessing and segmentation, feature extraction and selection, and model creation and validation. We achieve promising results during an analysis of more than 3000 classified welds using a model tree as a predictor with a success rate of 93 %. In the future, we hope to validate our model with unseen welding data and implement it in a real world application.