Control and Systems Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.


Position measurement is an essential process of mobile robot navigation. In this research, a Kalman Filter is applied to locating a mobile robot furnisher with an encoder and accelerometer. The accelerometer updates its position off-hand. It has an acceptable short period of stability. However, this stability will be decreased over time. The odometry model is utilized to measure the mobile robot's position and heading angle using encoders equipped with the wheels of the mobile robot. Moreover, the odometry model's errors exist because of the wheel rotating speed's integrative nature and non-systematic errors. In this work, the mobile robot position estimation in closed environments was studied. In order to obtain the optimal estimation, a Kalman filter was used to estimate mobile robots' position and velocity, where the Kalman filter has been designed for better assessment of the mobile robot position. The suggested configuration collects accelerometer and odometry reading to assure more delicate position knowledge than standalone odometry or accelerometer. The proposed method's position error has an acceptable level that is less than (0.2 m) for both easy and difficult paths.


  • Wheel encoders and accelerometer sensors were used with a Kalman filter to estimate the mobile robot position.
  • A modified Kalman filter was used to find the mobile robot position depending on the error between the predicted position produced by sensors and the position produced by a reference path.
  • The modification of the Kalman filter consists of choosing a variable process covariance matrix to produce a better Kalman gain value.  


Main Subjects

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