Genetic Programming Control of an Articulated Robotic Manipulator

Abstract

The inverse kinematics problem is one of the most important and challenging within articulated robotic manipulator scenarios. Classical mathematical techniques in solving the solutions to robotic kinematics control problems involve meticulous and time-consuming operations. There are no general methods in solving inverse kinematics problems or good practical solutions. Computer aided modeling methods have been developed in the absence of such straightforward numerical solutions, however they can also be computationally expensive. Recently, genetic programming approaches have been studied in robotic manipulator kinematics problems, where the inverse kinematic equations and joint variables are evolved simultaneously. An approach based on the genetic programming paradigm where the joint variables are evolved and used with an existing forward kinematics model is proposed and is used to control an articulated robotic manipulator with 5 degrees of freedom. Results show a successful controller can be evolved, where a number of optimal solutions are discovered initially and the solution that involves the least energy cost is selected.