Inverse kinematics of arbitrary robotic manipulators using genetic algorithms

In this paper we present a unified approach for solving the inverse kinematics problem of an arbitrary robotic manipulator based on Genetic Algorithms. The fitness function we use in our algorithm does a multi-objective optimization satisfying both the position and orientation of the end-effector. As an example we show how this approach can be used for computing the motion of an n-R robotic manipulator following a specified end-effector path. To avoid unnecessary manipulator undulations in case of a redundant design, we thereby introduce a third objective to our fitness function minimizing the discrete joint velocities. Unlike Jacobian-based solutions our approach deals efficiently with redundant designs and singularities.