• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.723-730
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• 2015

In this paper, a location tracking compensation algorithm based on the Least-Squares Method ($LCA_{LSM}$) was proposed to improve the autonomous tracking efficiency for the docent robot in exhibition hall, and the performance of the $LCA_{LSM}$ is analyzed by several practical experiments. The proposed $LCA_{LSM}$ compensates the collected location coordinates for the robot using the Least-Squares Method (LSM) in order to reduce the cumulated errors that occur in the Encoder/Giro sensor (E/G) and to enhance the measured tracking accuracy rates in the autonomous tracking of the robot in exhibition hall. By experiments, it was confirmed that the average error reduction rates of the $LCA_{LSM}$ are higher as 4.85% than that of the $LCA_{KF}$ in Scenario 1 (S1) and Scenario 2 (S2), respectively on the location tracking. In addition, it was also confirmed that the standard deviation in the measured errors of the $LCA_{LSM}$ are much more low and constant compared to that of the E/G sensor and the $LCA_{KF}$ in S1 and S2 respectively. Finally, we see that the suggested $LCA_{LSM}$ can execute more the stabilized location tracking than the E/G sensors and the $LCA_{KF}$ on the straight lines of S1 and S2 for the docent robot.