Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics
Affiliation(s)
Institute of Technology, Lebanese University, Saida, Lebano. Institute of Technology, Lebanese University, Saida, Lebanon.
Institute of Technology, Lebanese University, Saida, Lebano. Institute of Technology, Lebanese University, Saida, Lebanon.
ABSTRACT
One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.
One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.
Cite this paper
nullB. Daya, S. Khawandi and M. Akoum, "Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics," Journal of Software Engineering and Applications, Vol. 3 No. 3, 2010, pp. 230-239. doi: 10.4236/jsea.2010.33028.
nullB. Daya, S. Khawandi and M. Akoum, "Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics," Journal of Software Engineering and Applications, Vol. 3 No. 3, 2010, pp. 230-239. doi: 10.4236/jsea.2010.33028.
References
[1] B. Choi and C. Lawrence, “Inverse kinematics problem in robotics using neural networks,” National Aeronautics and Space Administration, Lewis Research Center, Cleveland, 1992. [2] R. K?ker, C. ?z, T. ?akar, and H. Ekiz , “A study of neural network based inverse kinematics solution for a three-joint robot,” Robotics and Autonomous Systems, Vol. 49, pp. 227–234, 2004. [3] L. Wei, H. Wang, and Y. Li , “A new solution for inverse kinematics of manipulator based on neural network,” Machine Learning and Cybernetics, Vol. 2, pp. 1201–1203, 2003. [4] J. Guo and V. Cherkassky, “A solution to the inverse kinematic problem in robotics using neural network processing”, International Joint Conference on Neural Networks, Vol. 2, pp. 299–304, 1989. [5] D. Pham, M. Castellani, and A. Fahmy “Accountability learning the inverse kinematics of a robot manipulator using the Bees Algorithm,” 6th IEEE International Conference on Industrial Informatics, pp. 493–498, 2008. [6] E. Gallaf , “Multi-fingered robot hand optimal task force distribution: Neural inverse kinematics approach,” Robotics and Autonomous Systems, Vol. 54, No. 1, pp. 34–51, 2006.
[1] B. Choi and C. Lawrence, “Inverse kinematics problem in robotics using neural networks,” National Aeronautics and Space Administration, Lewis Research Center, Cleveland, 1992. [2] R. K?ker, C. ?z, T. ?akar, and H. Ekiz , “A study of neural network based inverse kinematics solution for a three-joint robot,” Robotics and Autonomous Systems, Vol. 49, pp. 227–234, 2004. [3] L. Wei, H. Wang, and Y. Li , “A new solution for inverse kinematics of manipulator based on neural network,” Machine Learning and Cybernetics, Vol. 2, pp. 1201–1203, 2003. [4] J. Guo and V. Cherkassky, “A solution to the inverse kinematic problem in robotics using neural network processing”, International Joint Conference on Neural Networks, Vol. 2, pp. 299–304, 1989. [5] D. Pham, M. Castellani, and A. Fahmy “Accountability learning the inverse kinematics of a robot manipulator using the Bees Algorithm,” 6th IEEE International Conference on Industrial Informatics, pp. 493–498, 2008. [6] E. Gallaf , “Multi-fingered robot hand optimal task force distribution: Neural inverse kinematics approach,” Robotics and Autonomous Systems, Vol. 54, No. 1, pp. 34–51, 2006.