A neuro-genetic algorithm approach for solving the inverse kinematics of robotic manipulators

The inverse kinematics solution of a robotic manipulator requires the solution of non-linear equations having transcendental functions and involving time-consuming calculations. Artificial neural networks with their massively parallel architecture are natural candidates for providing a solution to this problem. In this work, a neuro-genetic algorithm approach is used to obtain the inverse kinematics solution of a robotic manipulator. A multi-layered feed-forward neural network architecture is used. The weights of the neural network are obtained during the training phase using a real-coded genetic algorithm. This training algorithm does not suffer from the usual drawbacks of the backpropagation learning algorithm. The approach is used to obtain the inverse kinematics solution of a planar robotic manipulator.