Learning-Based Analysis of a New Wearable 3D Force System Data to Classify the Underlying Surface of a Walking Robot

Biped humanoid robots that operate in real-world environments need to be able to physically recognize different floors to best adapt their gait. In this work, we describe the preparation of a dataset of contact forces obtained with eight force tactile sensors for determining the underlying surface of a walking robot. The data is acquired for four floors with different coefficient of friction, and different robot gaits and speeds. To classify the different floors, the data is used as input for two common computational intelligence techniques (CITs): Artificial neural network (ANN) and extreme learning machine (ELM). After optimizing the parameters for both CITs, a good mapping between inputs and targets is achieved with classification accuracies of about 99%.