Tactile sensing has recently attracted significant research interest in robotics. Despite the fact that tactile sensors provide temporal sequences of readings, state-of-the-art material recognition approaches are episodic, i.e. a whole sequence of readings is processed to identify the material. Based on vibration frequency response, this work presents an online identification technique using recursive estimation of the probability of identifying a set of materials, i.e. casting the classification problem as a hidden Markov model (HMM) state estimation problem. This allows for faster identification of most materials and does not require several exploratory movements. Our results show that when enough evidence is gathered, the system eventually achieves perfect recognition of our experimental set of 34 materials with an average identification time of ≤ 0.5 seconds. To prove the accuracy of this method, we also conducted a comparative experiment with commonly used machine learning algorithms for material identification such as k-Nearest Neighbour(KNN), an Artificial Neural Network(ANN) and Support Vector Machine(SVM).
|Title of host publication||2016 International Joint Conference on Neural Networks (IJCNN)|
|Publication status||Published - 2016|
|Event||2016 International Joint Conference on Neural Networks - Vancouver, Canada|
Duration: 24. Jul 2016 → 29. Jul 2016
|Conference||2016 International Joint Conference on Neural Networks|
|Period||24/07/2016 → 29/07/2016|