Publication

Abstract : Objective of this paper is to reduce the error in the estimation of the position of the robot. The localization of robot is a critical aspect in making them completely autonomous. Hence accuracy in estimating the position of the robot is of paramount importance. In order to make decisions without direct manual instructions the robot has to be aware of its position in the environment along with the situation at hand. For this effect the robots are built with sensors specifically designed to suit the need of the environment to gain feedback from the surrounding thereby obtaining data about its placement and the conditions around it. It is here that the problem of accuracy arises as there are uncertainties in both the feedback system as well as controlling system of the robot. To make a logical decision, the errors have to be minimized by optimally combining the data received from all the sources. The estimation theory provides us with a solution in the form of Kalman filter algorithm. It is an adaptive filter that combines uncertain data to obtain valid values of output required. It is seen that as SNR increases MSE in the position of the robot decreases thus accuracy increases. In this paper we concentrate on the issue of robot localization in closed space whose dimensions are previously known. We model the localization process as a linear phenomenon, as Kalman filter algorithm can only be used for linear systems. Using MATLAB Simulink we simulate our model to verify the concepts and validate the use of this filter in accurately determining the position of robot.