Analysis and Design Optimization of a Robotic Gripper Using Multiobjective Genetic Algorithm

Abstract

Robot gripper design is an active research area due to its wide spread applicability in automation, especially for high-precision micro-machining. This paper deals with a multiobjective optimization problem which is nonlinear, multimodal, and originally formulated. The previous work, however, had treated the actuator as a blackbox. The system model has been modified by integrating an actuator model into the robotic gripper problem. A generic actuation system (for example, a voice coil actuator) which generates force proportional to the applied voltage is considered. The actuating system is modeled as a stack consisting of the individual actuator elements arranged in series and parallel arrays in four different combinations. With the incorporation of voltage into the problem, which is related to both actuator force and manipulator displacement, the problem becomes more realistic and can be integrated with many real-life gripper simulations. Multiobjective evolutionary algorithm is used to solve the modified biobjective problem and to optimally find the dimensions of links and the joint angle of a robot gripper. A force voltage relationship can be obtained from each of the nondominated solutions which helps the user to determine the voltage to be applied depending on the application. An innovization study is further carried out to find suitable relationships between the decision variables and the objective functions.