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Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis

on Thu, 09/29/2016 - 19:51
TitleTowards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis
Publication TypeConference Paper
Year of Publication2009
AuthorsAntonelo EA, Schrauwen B
Conference NameProceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Abstract

Biological systems such as rats have special brain structures which process spatial information from the envi- ronment. 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.

DOI10.1109/ICSMC.2009.5346617