Comparing IMU-equipped Parallel and Flexible Grippers for Tactile Object Classification

This study presents a comparative analysis of two robotic hand prototypes: one with parallel grippers and the other with flexible grippers featuring articulated finger phalanges. The objective is to determine the most suitable model for manipulating objects in unstructured environments. We identified functional requirements and analyzed data from tactile sensors mounted on the robotic fingertips. We identified objects based on captured grasp and sensor data using artificial intelligence techniques. During pattern recognition exploration, data were collected via serial communication from sensors connected to a microcontroller. Machine learning algorithms with the AutoML technique were used to evaluate the performance of each gripper. The results showed that the hand with parallel grippers outperformed the flexible one in terms of accuracy, recall, precision, F1-score, achieving an average accuracy of 90.95%, compared to 51.10% for the flexible hand. These findings highlight the importance of selecting appropriate robotic grippers for superior object recognition performance.