Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Evolutionary Multi-Objective Trajectory Planning for 7 DOF Robot


Affiliations
1 Department of Computer Science & Engineering, J.J. College of Engineering and Technology, Thiruchirappalli-620009, Tamilnadu, India
     

   Subscribe/Renew Journal


This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (7 dof seriel link robot). The optimization model considers the nonlinear manipulator dynamics, actuator constraints and joint limits. The problem has 2 objective functions, 147 variables and 42 constraints. Two evolutionary algorithms namely Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) have been used for the optimization. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and DE are compared and analyzed.

Keywords

Multi-Objective Optimal Trajectory Planning, Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II), Differential Evolution (DE).
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 217

PDF Views: 5




  • Evolutionary Multi-Objective Trajectory Planning for 7 DOF Robot

Abstract Views: 217  |  PDF Views: 5

Authors

S. Mahalakshmi
Department of Computer Science & Engineering, J.J. College of Engineering and Technology, Thiruchirappalli-620009, Tamilnadu, India
R. Sumathi
Department of Computer Science & Engineering, J.J. College of Engineering and Technology, Thiruchirappalli-620009, Tamilnadu, India

Abstract


This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (7 dof seriel link robot). The optimization model considers the nonlinear manipulator dynamics, actuator constraints and joint limits. The problem has 2 objective functions, 147 variables and 42 constraints. Two evolutionary algorithms namely Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) have been used for the optimization. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and DE are compared and analyzed.

Keywords


Multi-Objective Optimal Trajectory Planning, Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II), Differential Evolution (DE).