Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot

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.

Automated summary

The study aims to implement surface recognition through walking-pattern classification using a support vector machine (SVM) and time-domain feature descriptors. By extracting real-time dynamic force-sensing data streams and applying multiple binary SVM classifiers, the developed cost-efficient and accurate surface-recognition system is valuable for ensuring safe indoor locomotion for biped robots and enhancing their understanding of the human living environment.