Visualizing and Analyzing Machine-soil Interactions using Computer Vision

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Abstract

This work presents an experimental method for visualizing and analyzing machine-soil interactions, namely the soil optical flow technique (SOFT). SOFT uses optical flow and clustering techniques to process images of soil interacting with a wheel or tool from photos taken through a glass wall of a soil bin. It produces results that are far richer than past approaches that utilized long-exposure photography. It achieves a performance comparable to particle image velocimetry or particle tracking velocimetry, but without the need for specialized measurement equipment or specially marked soil particles. The processing technique demonstrates robustness to different soil types. Ground-truth and cross-validation experiments exhibit subpixel accuracy in estimating soil motions. An example of an application of this technique for field robotics research is the detailed study of push-rolling for slope climbing and soft soil traverse. Push-rolling advances a vehicle by rolling a subset of its wheels while changing its wheelbase to keep the other wheels static and pushing against the ground. Experiments show that push-rolling achieves higher net traction than conventional rolling. Observing the two aspects of push-rolling (rolling and horizontal pushing) using SOFT shows that they result in entirely different forms of soil shearing (“grip failure” and “ground failure,” respectively). SOFT also demonstrates how the direction of soil motion is more efficiently utilized for horizontal thrust by pushing than conventional rolling. Ongoing work utilizing SOFT has also demonstrated its potential use in studying excavation tool interactions, the effects of grousers on wheel efficiency, as well as a variety of other wheel-soil interactions.

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