Real Time Direct Kinematic Problem Computation of the 3PRS robot Using Neural Networks

Abstract

The reliable calculation of the Direct Kinematic Problem (DKP) is one of the main challenges for the implementation of Real-Time (RT) controllers in Parallel Robots. The DKP estimates the pose of the end effector of the robot in terms of the sensors placed on the actuators. However, this calculation requires the use of time-consuming numerical iterative procedures. Artificial Neural Networks have been proposed to implement the complex DKP equation mapping due to their universal approximator property. However, the proposals in this area do not consider the Real Time implementation of the ANN based solution, and no approximation error vs computational time analysis is carried out. In this work, a methodology that uses Artificial Neural Networks (ANNs) to approximate the DKP is proposed. Based on the 3PRS parallel robot, a comprehensive study is carried out in which several net- work configurations are proposed to approximate the DKP. Moreover, to demonstrate the effectiveness of the approach, the proposed networks are evaluated considering not only their approximation capabili- ties, but also their Real Time performance in comparison with the traditional iterative procedures used in robotics.