Neural Networks for Mobile Robot Inverse Kinematics

Abstract

Inverse kinematics refers to determining the forces that must be applied to a particular system to result in a desired configuration of the system. In robotics, inverse kinematics means calculating the robot actuator movements necessary to make a robot perform a specific task. Calculating the inverse kinematics using traditional methods is a complex and time consuming task. This paper reports on a novel approach to predicting the inverse kinematics of a mobile robot using Neural Networks (NNs). The main advantage of using artificial intelligence to determine inverse kinematics is that minimal human input and intervention is required. This research makes use of Feedforward NNs to predict the motor velocities and the time that they must be maintained to make the robot reach a specified destination. Inertia and friction are automatically incorporated into the NN predictions. Experimental evidence is presented which shows that the proposed approach can successfully produce commands which allow the robot to traverse a given path.