# A Haptic-Based Proximity Sensing System for Buried Object in Granular Material

arXiv: https://arxiv.org/abs/2411.17083 · HTML: https://mlsanity.com/paper/2411.17083/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning](https://mlsanity.com/paper/2412.02119/) (2024)

## Abstract (arXiv)

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.