用触觉反馈感知埋在颗粒物中的物体，提前0.5至7厘米发现目标。
A Haptic-Based Proximity Sensing System for Buried Object in Granular Material
- 利用颗粒物的破坏楔区特性与触觉信号变化，结合高斯过程回归识别近场
- 可在多种颗粒材料中提前0.5至7厘米感知地下物体，精度稳定
- 自适应调节参数，适合复杂环境下的探测任务，如排雷
在颗粒材料中对物体进行接近感知具有重要意义，尤其在排雷等应用中。然而，由于颗粒物不透明且性质复杂，现有接近传感器存在硬件成本高、用户理解困难等问题。本文提出一种基于触觉反馈的地下物体接近感知系统，通过研究并利用颗粒物特有的“破坏楔区”特性，结合高斯过程回归方法，识别由物体靠近引起的力信号变化，实现近场感知。此外，设计了一种新型探测轨迹，使探头能在颗粒物中实现大范围感知。系统还能自适应调整参数，在不同颗粒材料中保持鲁棒性。实验表明，该系统可在多种材料中提前0.5至7厘米感知地下物体。
原文摘要 · Abstract (English)
The proximity perception of objects in granular materials is significant, especially for applications like minesweeping. However, due to particles' opacity and complex properties, existing proximity sensors suffer from high costs from sophisticated hardware and high user-cost from unintuitive results. In this paper, we propose a simple yet effective proximity sensing system for underground stuff based on the haptic feedback of the sensor-granules interaction. We study and employ the unique characteristic of particles -- failure wedge zone, and combine the machine learning method -- Gaussian process regression, to identify the force signal changes induced by the proximity of objects, so as to achieve near-field perception. Furthermore, we design a novel trajectory to control the probe searching in granules for a wide range of perception. Also, our proximity sensing system can adaptively determine optimal parameters for robustness operation in different particles. Experiments demonstrate our system can perceive underground objects over 0.5 to 7 cm in advance among various materials.
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