Manufacturing Technology Today

R. Saravanan ¹, S. Ramabalan ², C. Balamurugan ³, A. Subash ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J. J. College of Engg. and Technology, Trichy, IN
3 Dept. of Production Engg., J. J. College of Engg. and Technology, Trichy, IN

Abstract

This paper presents a new general method fo r computing the optimal motions of industrial robot manipulators in the presence of fixed and moving obstacles. The mathematical model considers the nonlinear manipulator dynamics, actuator constraints, joint limits and obstacle avoidance. The problem considered has 5 objective functions, 88 variables and 21 constraints. Two evolutionary algorithms such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) techniques have been used for the optimization. Given the initial and final configurations, the trajectory is defined using B-spline function and is obtained through off-line computation for on-line operation. The obstacles are considered as objects sharing the same workspace performed by the robot. The obstacle avoidance is expressed in terms of the distances between potentially colliding parts and the motion is represented using translation and rotational matrices. Numerical application involving an industrial manipulator (Adeptone XL robot) is presented. The results obtained from NSGA-II and DE are compared and analyzed. A comprehensive user-friendly general-purpose software package has been developed for the DE algorithm using VC++ to obtain the optimal solutions.

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R. Saravanan ¹, S. Ramabalan ², P. Sasikumar ³, P. Sriram ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, Tamil Nadu, IN
2 J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN
3 Dept, of Production Engg., J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN

Abstract

This paper concerns with the use of intelligent techniques such as Genetic Algorithm (GA) and Differential Evolution (DE) to find optimum geometrical dimensions of a robot gripper. The problem is finding a combined objective function, which has five objectives, seven constraints and five variables. The objective functions are difference between maximum and minimum griping forces, force transmission ratio between gripper actuator and gripper ends, shift transmission ratio between gripper actuator and gripper ends, length of all elements of gripper and efficiency of gripper mechanism. The problem is dealt with three cases. A very original, new optimization model is derived and used. Also, a comprehensive user-friendly general-purpose software package has been developed to obtain the optimal parameters using the proposed DE algorithm.

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R. Saravanan ¹, S. Ramabalan ², M. Thilak ²

Affiliations
1 Dept. of Mechanical Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J.J. College of Engg. and Technology, Trichy, IN

Abstract

The minimum-time path for a robot arm has been a long standing and unsolved problem of considerable interest. This paper presents two intelligent optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) for obtaining minimum time paths for robot manipulators. To illustrate the proposed methods, time optimal trajectories for a two-link IBM planar robot Is considered in this work. The results obtained using the proposed GA and PSO are compared with neural network results equations. It Is proved from this paper that the proposed intelligent optimization algorithms can be used successfully to find optimal travel time by approximating the robot inverse dynamics. Also both proposed GA and PSO methods give better results than the neural network.

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