Reference : Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis
Antonelo, Eric Aislan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Schrauwen, Benjamin [> >]
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)
2009 IEEE International Conference on Systems, Man and Cybernetics
11-10-2009 to 14-10-2009
[en] Biological systems such as rats have special brain structures which process spatial information from the environment. They have efficient and robust localization abilities provided by special neurons in the hippocampus, namely place cells. This work proposes a biologically plausible architecture which is based on three recently developed techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The bottom layer of our RC-SFA architecture is a reservoir of recurrent nodes which process the information from the robot's distance sensors. It provides a temporal kernel of rich dynamics which is used by the upper two layers (SFA and ICA) to autonomously learn place cells. Experiments with an e-puck robot with 8 infra-red sensors (which measure distances in [4-30] cm) show that the learning system based on RC-SFA provides a self-organized formation of place cells that can either distinguish between two rooms or to detect the corridor connecting them.

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