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：近距感知能够在不接触的情况下检测物体的存在。然而，由于颗粒材料缺乏视觉特性且性质复杂，相关研究极少探讨其在其中的近距感知应用。本文基于三种颗粒现象（流化、阻塞和失效楔形区），提出了一种嵌入颗粒材料的自主近距感知系统。该系统无需视觉或触觉，即可实时（至少约20赫兹）自动感知颗粒材料下埋藏的物体，并在不同颗粒中探测前方0.5至7厘米距离内的物体。我们借鉴一种常见的颗粒流化技术，引入了一种结合直线与圆周运动的新型螺旋轨迹，用于探头在颗粒材料中的耙动。基于探头耙动过程中前方失效楔形区发生颗粒阻塞时力值上升的观察，我们采用高斯过程回归持续学习并预测力模式，通过检测颗粒阻塞导致的力异常来判断埋藏物体的近距感知。最后，我们将该系统与贝叶斯优化算法引导的探索策略相结合，在不接触或挖掘的情况下，成功定位地下物体并描绘其分布。这项工作提供了一种简单可靠的方法，具有在无需人工干预的外星行星上安全建造栖息基础设施的潜力。