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.

翻译：近距感知无需接触即可检测物体的存在。然而，由于颗粒材料缺乏视觉特性且性质复杂，鲜有研究探索其中近距感知的应用。本文提出一种基于颗粒材料三种现象（流态化、堵塞和失效楔形区）的自主近距感知系统（GRAINS）。该系统能够以至少约20赫兹的实时速率自动感知颗粒材料下方的掩埋物体，并在不同颗粒介质中超前0.5至7厘米感知这些物体，且无需依赖视觉或触觉。受常见颗粒流态化技术启发，我们为探针在颗粒材料中的耙动引入了一种结合线性与圆周运动的新型螺旋轨迹。基于探针耙动过程中其前方失效楔形区内颗粒堵塞引发力值上升的观测，我们采用高斯过程回归持续学习并预测力模式，同时检测由颗粒堵塞导致的力异常，从而识别掩埋物体的近距感知。最后，我们将GRAINS应用于基于贝叶斯优化算法的引导探索策略，成功定位地下物体并通过近距感知勾勒其分布，无需接触或挖掘。该工作提供了一种简单可靠的方法，具有在无人工干预的外星球建造人类栖息基础设施中进行安全操作的潜力。