Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis
Title | Towards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis |
Publication Type | Conference Proceedings |
Year of Conference | 2009 |
Authors | Antonelo EA, Schrauwen B |
Conference Name | Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Pagination | 3818–3823 |
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. |
DOI | 10.1109/ICSMC.2009.5346617 |