Surface-Property Recognition With Force Sensors for Stable Walking of Humanoid Robot

In this paper, we propose a surface-identification system for stable humanoid-robot walking on various types of surfaces using force sensors mounted under the robot feet. For experimental identification analysis of the surface condition, we measured the sensor-output data using five different types of test surfaces. To achieve fast dynamic recognition capability of changing surface conditions, we applied an overlapped sliding-window method for the incoming sensor-data stream to generate dynamically four distinguishable well-known features from the raw sensor data. The multi-class k-nearest-neighbor (MC-kNN) classifier rather than a binary classifier is used for online classification of the measured robot-walking pattern and classification-accuracy evaluation. Further, we combine the four studied feature descriptors into a fused multi-feature descriptor rather than invoking each feature descriptor independently, increasing the classification performance. Our analysis results verify that 90.4% maximum overall accuracy with 91.49% average precision can be achieved, demonstrating the realization of a better cost-performance trade-off than in other previous research works. The obtained results are useful for balancing the robot body through optimized controlling of the robot motors according to the recognized different surfaces during robot motion.