Event detection and localization for small mobile robots using reservoir computing

Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

Videos showing data generation for event detection and localization:

Publications

1. Eric Antonelo, Benjamin Schrauwen and Dirk Stroobandt Event detection and localization for small mobile robots using reservoir computing NEURAL NETWORKS, Vol. 21(6), pp. 862-871 (2008)  
2. Eric Antonelo, Benjamin Schrauwen, Xavier Dutoit, Dirk Stroobandt and Marnix Nuttin Event detection and localization in mobile robot navigation using reservoir computing Proceedings of the International Conference on Artificial Neural Networks (ICANN), pp. 660-669 (2007)