Multi-objective reliability-based robust design optimization of robot gripper mechanism with probabilistically uncertain parameters

Abstract

Optimization without considering uncertainty of system parameters can lead to potentially high-risk solutions. In order to take into account the effect of those uncertain parameters of the system, multi-objective reliability-based robust design optimization using the sensitivity-assisted Monte Carlo simulation method is developed in this study. In this way, probabilistic design of a robot gripper mechanism performed based on a multi-objective uniform-diversity differential evolution algorithm. The gripper mechanism used for design optimization has been extensively studied deterministically in the literature. However, it has been shown that by considering just 1 % uncertainty in system parameters, the stochastic performance analysis of the gripper configuration shows large variations in objective functions as well as failure of some constraints of the mechanism. The objective functions that have been considered are the mean and standard deviation of the difference between the maximum and minimum gripping forces and the transmission ratio of actuated and experienced gripping forces at gripper ends. In order to achieve desired reliability level, the mechanism functional constraints are converted to probabilistic ones. Furthermore, the comparison of the obtained results using the method of this paper with those obtained using deterministic approach shows a significant improvement in robustness and reliability behavior of gripper mechanism.