Materials Classification via Piezoelectric Sensors for Robotic Grippers

Robotic arms are gradually becoming applicable in every industry. A majority of these are employed in the sorting and identification of objects at assembly lines. Therefore, there is a need for accurate, sensitive, and efficient sensing mechanisms installed on the robotic arm. This research presents an efficient mechanism by using piezoelectric sensors mounted on the robotic gripper. The sensors help in the identification and categorization of objects based on their hardness. The proposed system successfully achieved an average accuracy of 80% over the categorization of three different specimens including hard (wood), intermediate (thermopole), and soft (sponge) materials.