Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS AND DISCUSSION: 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.