Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot
Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot
复制标题
DOI：
10.1109/jsen.2021.3059099
复制
发表时间：
2021-04
影响因子：
4.3
通讯作者：
A. Luo;S. Bhattacharya;S. Dutta;Y. Ochi;M. Miura-Mattausch;Jian Weng;Yicong Zhou;H. Mattausch-H.-Mattau
中科院分区：
文献类型：
--
作者：
A. Luo;S. Bhattacharya;S. Dutta;Y. Ochi;M. Miura-Mattausch;Jian Weng;Yicong Zhou;H. Mattausch-H.-Mattau
实时的表面识别已经成为保证智能双足机器人在复杂的人类生活环境中安全行走的关键因素。这项工作的目的是通过将必要的硬件限制在成本经济的微处理器和单一类型的力传感器上，通过步行模式分类实现用于表面识别的传感解决方案的广泛成本效益。在实验分析中，我们使用支持向量机和四个时域特征描述符，即幅度均值(MA)、绝对值积分(IAV)、方差(VAR)和均方根值(RMS)，探讨了步行模式分类的性能。在在线模式分类中，采用基于实时重叠窗口的方法提取动态力-感官数据流。多个二进制支持向量机分类器用于解决多类分类问题，由于具有较高的精度和相对较小的硬件实现复杂度，允许一对一(OVO)策略同时利用上述四个单独的特征描述符的强度。对250个样本/表面的实验结果表明，光滑木材、粗泡沫、光滑泡沫、厚地毯和薄地毯的平均准确率为93.8%，平均准确率为93.7%，召回率分别为98.8%、91.6%、82.0%、98.0%和98.0%。只有动态力传感数据被用于10倍交叉验证，从而实现了0.73ms/步长的高处理速度。所开发的高性价比、准确的表面识别系统可用于保证双足机器人在室内的安全运动，并可通过增加其感知多样性来帮助机器人更好地了解人类的生存环境。
Real-time surface recognition has become a critical factor for ensuring safe walking of intelligent biped robots in a complex human living environment. This work aims at enabling wide cost-efficient implementation of sensing solutions for surface recognition via walking-pattern classification by restricting the necessary hardware to a cost-economic microprocessor and a single type of force sensors. For experimental analysis, we explored the walking-pattern classification performance using a framework which combines a support vector machine (SVM) and four time-domain feature descriptors, i.e., mean of amplitude (MA), integral of absolute value (IAV), variance (VAR), and root mean square (RMS). During the online pattern classification, the dynamical force-sensory-data stream was extracted using a real-time overlapped-window-based method. Multiple binary SVM classifiers were applied for solving the multi-class classification problem, due to the reasonably high accuracy and the relatively small complexity for hardware implementation, allowing simultaneous strength exploitation of above four individual feature descriptors with a one-versus-one (OVO) strategy. The experimental results, obtained with 250 samples/surface, verified 93.8% mean average precision, 93.7% average accuracy and recall rates of 98.8%, 91.6%, 82.0%, 98.0%, 98.0% for smooth wood, rough foam, smooth foam, thick carpet, and thin carpet, respectively. Only the dynamical force-sensing data were employed for a 10-fold cross validation, which enabled the high processing speed of 0.73 ms/stride. The developed cost-efficient and accurate surface-recognition system can be useful for ensuring safe in-door locomotion for the biped robot and can help the robot to better understand the human living environment by increasing its sensing diversity.