Artificial neural networks for autonomous robot control: reflective navigation and adaptive sensor calibration

Loffler A, Klahold J, Rückert U. Artificial neural networks for autonomous robot control: reflective navigation and adaptive sensor calibration. In: Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on. Vol 2. IEEE; 1999: 667-672.In this paper, we present the application of artificial neural networks to the control of a mobile, autonomous robot, which is acting in a totally unknown and - most importantly - dynamically changing environment. In particular, the employment of interacting 'simple', i.e. hand-designed, neural networks for navigation purposes is investigated as well as a variation of self-organizing maps for adaptive sensor calibration. We insofar take a pragmatic point of view as the minimal condition imposed on the developed algorithms is that they do well on a real system acting in a real environment. Hence, the design of all of the implemented neural networks is clearly motivated by their applicability. In this context, special considerations are dedicated to ensure robustness, real-time capability and memory resourcefulness. In order to practically demonstrate the obtained results, the minirobot Khepera is utilized as an experimentatory platform, which is - due to its small size - a versatile tool for scientific investigation

Implementing Neural Soft- And Hardware On The Autonomous Mini-robot Khepera

Loffler A, Klahold J, Heittmann A, Witkowski U, Rückert U. Implementing Neural Soft- And Hardware On The Autonomous Mini-robot Khepera. In: Microelectronics for Neural, Fuzzy and Bio-Inspired Systems, 1999. MicroNeuro '99. Proceedings of the Seventh International Conference on. IEEE Comput. Soc; 1999: 425-426.The applicability of neural networks to generate complex behaviour on autonomous systems is demonstrated both at soft- and hardware-level. In particular, the emergence of simple behaviors based on the Braitenberg approach, adaptive sensor calibration by self-organizing maps with a comparison between off- and online learning and a visualisation tool for a posteriori analysis are shown. It is also envisaged to present the working of embedded neural hardware as associative memory and self-organizing maps. In this connection, the mini-robot Khepera serves as an exemplary platfor

KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables

International audienceData provided by statistical models are commonly represented by textual, tabular or graphical form in documents (reports, articles, posters and presentations). These documents are often available in PDF format. Even though it makes accessing a particular information more difficult, it is interesting to process the PDF documents directly. We present KEFT, a solution in the statistical domain and we describe the fully functional pipeline to constructing a knowledge graph by extracting entities and relations from statistical Data Tables. We showcase how this approach can be used to construct a knowledge graph from different statistical studies