Abstract

In this study, a four-fingered robotic gripper was custom-designed and integrated with a UR5 robot arm to enable adaptive, real-time grasping of objects with varying shapes, sizes, and weights. Dynamic and static analyses were performed to validate the structural integrity, force distribution, and load-handling capacity of the gripper. The mechanical design incorporated lightweight honeycomb structures to maximize the strength-to-weight ratio, while under actuation minimized actuator complexity. Following structural validation, a closed-loop control algorithm was implemented using Force Sensing Resistor (FSR) feedback to regulate grasping force in real time. The system estimates object weight dynamically and adjusts the force threshold iteratively to ensure stability without exceeding the structural limits or causing object damage. Experimental validation using cylindrical, spherical, and rectangular objects demonstrated that tactile sensing significantly reduced excessive gripping force and improved stability, as quantified by a force reduction metric. The gripper achieved reliable handling of objects ranging from 0.025 to 5𝑘𝑔, enhancing the UR5 robot’s dexterity and versatility for industrial applications. Results suggest that incorporating tactile feedback and adaptive force control mechanisms greatly improve the performance and safety of robotic gripping systems. Future work will explore machine learning-based adaptive control strategies to extend the gripper's capabilities to a broader range of materials and surface textures. This approach offers a cost-effective, customizable solution for enhancing autonomous robotic manipulation in dynamic, unpredictable environments.