Surface identification using simple contact dynamics for mobile robots

This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved (90.0 and 94.6 percent for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot.