This paper describes a tactile probe designed for surface identification in a context of all-terrain low-velocity mobile robotics. The proposed tactile probe is made of a small metallic rod with a single-axis accelerometer attached near its tip. Surface identification is based on analyzing acceleration patterns induced at the tip of this mechanically robust tactile probe, while it is passively dragged along a surface. A training dataset was collected over ten different indoor and outdoor surfaces. Classification results for an artificial neural network were positive, with an 89.9% and 94.6% success rate for 1- and 4-s time windows of data, respectively. We also demonstrated that the same tactile probe can be used for unsupervised learning of terrains. For 1-s time windows of data, the classification success rate was only reduced to 74.1%. Finally, a blind mobile robot, performing real-time classification of surfaces, demonstrated the feasibility of this tactile probe as a guidance mechanism.
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