Author(s): Gurjeet Singh, Dr. Vijay Kumar Banga
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.1 - Issue 4 (June- 2012)
The inverse kinematics problem in robotics requires the determination of the joint angles for a desired position of the end-effector. The most important problem in robot kinematics and control is, finding the solution of Inverse Kinematics. If the joint structure of the manipulator is more complex the traditional method such as geometric, iterative and algebraic are inadequate. As the complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. For this under constrained and ill- conditioned problem we propose a solution based on structured neural networks that can be trained quickly. In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS with limited mathematical representation of the system. Computer simulations conducted on 2DOF and 3DOF robot manipulator shows the effectiveness of the approach. Workspace area of 2DOF and 3DOF is also shown .
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