Real-Time Adaptive Gripping Mechanism Using Object Classification and Feedback Control

Building on the foundation of our previous research, this study transitions from theoretical object classification to practical applications in adaptive gripping mechanisms. In the previous study, we introduced a TensorFlow-based machine learning model capable of classifying objects as hard or soft using visual data from image files or live camera input. The classification process addresses the initial challenge of identifying object types, and this research extends to the critical next step: enabling a robotic gripper to apply appropriate and dynamically adjusted pressure to objects based on their classification and stiffness characteristics.The core contribution of this study lies in integrating force-sensitive resistors (FSRs) into a robotic gripper design to facilitate real-time pressure sensing and feedback. FSRs are low-cost and reliable sensors capable of measuring the force applied to the contact. These sensors were strategically placed on the contact surfaces of the gripper to capture precise pressure data during gripping. This feedback, combined with the classification results, ensures that the gripper dynamically adjusts its force in real-time to handle a wide range of objects.The system architecture involves a microcontroller interfacing hardware with a TensorFlow-powered classification model running on a GPU-equipped computer. The workflow begins with an image input that is processed using the TensorFlow model to classify an object as hard or soft. This classification result is transmitted to a microcontroller that uses the data to determine the initial gripping force. As the gripper closes around an object, the FSR sensors provide continuous feedback on the applied pressure. If the sensed force deviates from the optimal value for the object type, the system dynamically recalibrates the motor torque to ensure secure and damage-free handling.To evaluate system performance, experiments are being conducted in which, to date, we have used 70 small objects categorized into hard and soft items. The TensorFlow model achieved a classification accuracy of 80.25% for both object classes combined. Pressure adjustment experiments demonstrated the ability of the system to maintain gripping forces within a ±12.75% to ±15.8% range of optimal values, minimizing damage to fragile objects while securely grasping more rigid ones. We believe that the ranges mentioned here will converge to lower values as more experiments are conducted and our system is refined. For example, glass cup required a maximum average gripping force of 13.41 N (corresponding FSR voltage reading of 3.97 V), whereas plush toys (flexible soft objects) required 4.16 N ((corresponding FSR voltage reading of 3.23 V), which is significantly lower than that required for glass cup (hard objects).This study bridges the gap between machine-learning models and real-world robotic applications by demonstrating how object classification can inform dynamic interactions with physical hardware. The integration of real-time feedback from FSR sensors introduces a level of adaptability that is crucial for applications in industrial automation, service robotics, and precision manufacturing.