# A conceptual approach to material detection based on damping vibration-force signals via robot

## TL;DR

This paper introduces a new method for detecting object materials using robot-based vibration and damping signals, achieving high accuracy.

## Contribution

A novel impact-based material detection approach using damping vibration-force signals and machine learning in a robotic setup.

## Key findings

- The method achieved 95.46% accuracy in detecting materials of ten different objects.
- Using damping and vibration features improved classification accuracy over traditional tactile methods.
- The system is suitable for industrial robot applications due to its robustness and performance.

## Abstract

Object perception, particularly material detection, is predominantly performed through texture recognition, which presents significant limitations. These methods are insufficient to distinguish between different materials with similar surface roughness, and noise caused by tactile movements affects the system performance.

This paper presents a straightforward, impact-based approach to identifying materials, utilizing the cantilever beam mechanism in the UR5e robot's artificial finger. To detect object material, an elastic metal sheet was fixed to a load cell with an accelerometer and a metal appendage positioned above and below its free end, respectively. After recording the damping force signal and vibration data from the load cell and accelerometer caused by the metal appendage's impact, features such as vibration amplitude, damping time, wavelength, and force amplitude were retrieved. Three machine-learning techniques were then used to classify the objects' materials according to their damping rates. Data clustering was performed using the deflection of the cantilever beam to boost classification accuracy.

Online object materials detection shows an accuracy of 95.46% in a study of ten objects [metals (steel, cast iron), plastics (foam, compressed plastic), wood, silicon, rubber, leather, brick and cartoon]. This method overcomes the limitations of the tactile approach and has the potential to be used in industrial robots.