Design and implementation of efficient intelligent robotic gripper

The gripper is one critical component of an industrial robot which is often useful in industrial environments for object grasping during handling process. In this paper, a gripper is designed and implemented to grasp unknown objects with different masses, dimensions, and coefficients of friction considering simplicity, wide range of objects and economy. The proposed grasping process during object lifting and handling is mainly based on the slip reflex principle, as applying insufficient force leads to object slipping, and dropping may occur. In the mean time, applying extra force during grasping may lead to object crushing. A new system controller scheme using fuzzy logic based on empirical investigation of the human hand skills is proposed to control the applied force on the object to avoid the risk of object crushing or dropping. Adaptive neuro-fuzzy inference system ANFIS is used to model the assigned robotic gripper based on input/output variables measurement. Reducing the distance of the slippage and process time can be achieved owing to the proposed robotic gripper as shown in experimental results.