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

TL;DR

This paper introduces a simple haptic-based proximity sensing system for buried objects in granular materials, utilizing particle interaction and machine learning to achieve effective underground detection.

Contribution

It presents a novel proximity sensing approach combining haptic feedback, particle failure zones, and Gaussian process regression for underground object detection.

Findings

Detects objects 0.5 to 7 cm underground across various materials.

Uses machine learning to interpret force signals for proximity detection.

Adapts parameters for robustness in different granular environments.

Abstract

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.