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 reallife 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.

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

“…As it can be seen in Table 10 the quality of the solutions between the algorithms is similar, showing that the reduction in the number of evaluations 1.1E41 9.2E51 0.0E+00 f 3 1.2E26 2.2E37 1.7E-08 f 4 1.9E02 3.2E71 4.6E-09 f 5 5.6E01 4.0E01 3.9E01 f 6 1.1E+02 1.2E+02 0.0E+00 f 7 1.1E03 7.8E04 6.5E04 f 8 8.9E+03 8.6E+03 0.0E+00 f 9 1.9E01 2.0E01 0.0E+00 f 1 0 7.9E06 4.2E07 0.0E+00 of fitness function in the PMIM, does not affect the final quality of the solutions.…”

Integrated Design and Optimization Method of an Asymmetric Hybrid Thrust Magnetic Bearing With Secondary Air-Gap

“…There are varieties of optimal methods to choose, such as GA and particle swarm optimization (PSO). GA is one of the global optimization algorithms, which has been widely used in many areas in recent years [31][32][33]. In [31], optimization of a robotic gripper using multi-objective GA was performed.…”

“…GA is one of the global optimization algorithms, which has been widely used in many areas in recent years [31][32][33]. In [31], optimization of a robotic gripper using multi-objective GA was performed. In [32], to improve the classification performance of a polynomial neural network, a novel approach with real-coded GA was proposed.…”

Robotic perception and grasp in unstructured environments

“…The force/torque transmission from the actuator to robotic fingers [110] determines the grasping forces/torques that exert an object. The models of force/torque transmission are built for optimizing the dimensions of robotic fingers, to a lesser extent, to reduce the subjective considerations from the topology of anthropomorphic hand.…”

“…Finally, to achieve the maximum objective value, the fitness function with the geometric constraints is If the grasp is successful, 3 becomes 1 and otherwise 0. Since the actuated force is a constant in the fitness function and different from the reference[110], it has no effect on the change amount of and will not be optimized.The formula is amplified times to compensate the difference of 1 and 1 in the numeral magnitude. In this work, the actuator in the gripper prototype can execute a known force to the active tendon based on the positive transmission mechanism.…”