  Proximity sensing detects an object's presence without contact. However,
research has rarely explored proximity sensing in granular materials (GM) due
to GM's lack of visual and complex properties. In this paper, we propose a
granular-material-embedded autonomous proximity sensing system (GRAINS) based
on three granular phenomena (fluidization, jamming, and failure wedge zone).
GRAINS can automatically sense buried objects beneath GM in real-time manner
(at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different
granules without the use of vision or touch. We introduce a new spiral
trajectory for the probe raking in GM, combining linear and circular motions,
inspired by a common granular fluidization technique. Based on the observation
of force-raising when granular jamming occurs in the failure wedge zone in
front of the probe during its raking, we employ Gaussian process regression to
constantly learn and predict the force patterns and detect the force anomaly
resulting from granular jamming to identify the proximity sensing of buried
objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided
exploration strategy to successfully localize underground objects and outline
their distribution using proximity sensing without contact or digging. This
work offers a simple yet reliable method with potential for safe operation in
building habitation infrastructure on an alien planet without human
intervention.
