Design optimization of robot grippers using teaching-learning-based optimization algorithm 

 Abstract

 This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm 
 to obtain the optimum geometrical dimensions of a robot gripper. Design of robot grippers is 
 an important aspect of the overall robot performance and the most demanding process in any 
 robot system to match the need for the production requirement. Robot grippers are the end 
 effectors used to grasp and hold the objects. A gripper acts as the bridge between the robot 
 arm and the environment around it. Five objectives, namely the difference between the maximum 
 and minimum gripping forces, the force transmission ratio, the shift transmission ratio, length 
 of all the elements of the gripper, and the effort of the gripper mechanism are considered as 
 objective functions. The problem has seven design variables, including six variables as dimensions 
 of the gripper and one variable as the angle between two links of the gripper. Three robot grippers 
 are optimized. Computational results shows that the TLBO algorithm is better or competitive to other 
 optimization algorithms recently reported in the literature for the considered problem.