Contact Classification for Agriculture Manipulation in Cluttered Canopies

In this paper, the authors present a novel way to classify contact objects using audio signals in a highly cluttered canopy environment for agriculture manipulation. Rather than solely relying on visual data to represent the dense canopies as obstacles, we investigate whether robot can observe latent properties about safe interactions such as brushing against leaves using audio signals. We developed a hand-held device to facilitate the data collection process to distinguish between three classes: leaf, twig, trunk. Of the time domain, frequency domain, and cepstrum representations (MFCC), MFCC comparisons showed the most distinguishable patterns across the classes. The provided results present a promising direction to expand this research to leverage deep learning networks to consistently classify the extracted audio inputs that can lead to safe and robust agriculture manipulation.