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%.

中文翻译：

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基于学习的新型可穿戴 3D 力系统数据分析以对步行机器人的底层表面进行分类

在现实世界环境中运行的双足类人机器人需要能够在物理上识别不同的楼层，以最好地适应它们的步态。在这项工作中，我们描述了使用八个力触觉传感器获得的接触力数据集的准备工作，用于确定步行机器人的下表面。数据是针对具有不同摩擦系数、不同机器人步态和速度的四个楼层获取的。为了对不同楼层进行分类，数据被用作两种常见计算智能技术 (CIT) 的输入：人工神经网络 (ANN) 和极限学习机 (ELM)。在优化两个 CIT 的参数后，输入和目标之间的良好映射实现了大约 99% 的分类精度。