Evolutionary multi criteria design optimization of robot grippers

Authors:
R. Saravanan;S. Ramabalan;N. Godwin Raja Ebenezer;C. Dharmaraja
Affiliations:
Department of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore 641006, Tamil Nadu, India;Faculty of CAD/CAM (P.G. Course), J.J. College of Engineering and Technology, Thiruchirapalli 620009, Tamil Nadu, India;Department of Mechanical Engineering, Einstein College of Engineering, Tirunelveli, 627012, Tamil Nadu, India;School of mechanical and building sciences, VIT University, Vellore, Tamil Nadu, 632014, India
Venue:
Applied Soft Computing
Year:
2009

Citing 2
Cited 4

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons

Artificial Intelligence Review

The Similarity Design of Heavy Forging Robot Grippers

ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Multi-criteria decision making approach based on immune co-evolutionary algorithm with application to garment matching problem

Expert Systems with Applications: An International Journal
A multi-objective approach for the motion planning of redundant manipulators

Applied Soft Computing
Evolutionary algorithm characterization in real parameter optimization problems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper explores the use of intelligent techniques to obtain optimum geometrical dimensions of a robot gripper. The optimization problem considered is a non-linear, complex, multi-constraint and multicriterion one. Three robot gripper configurations are optimized. The aim is to find Pareto optimal front for a problem that has five objective functions, nine constraints and seven variables. The problem is divided into three cases. Case 1 has first two objective functions, the case 2 considers last three objective functions and case 3 deals all the five objective functions. Intelligent optimization algorithms namely Multi-objective Genetic Algorithm (MOGA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE) are proposed to solve the problem. Normalized weighting objective functions method is used to select the best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure (SSM) and ratio of non-dominated individuals (RNIs)) are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead (OO) and algorithm effort are used to find the computational effort of MOGA, NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.