Abstract
Accurate localization of mobile robots to locate its position and orientation is of key importance since it enables a mobile robot to navigate properly in any given environment. Various techniques of localization used are such as GPS/GNSS, IMU sensors or by using odometric measurements. However each of these techniques suffers from various drawbacks. Dead-reckoning (DR) is a popular client to get precise localization information. DR estimates the current position based on the previous positions observed over a span of time. However DR depends on encoder and odometric information which are subject to major errors due to surface roughness, wheel slippage and tolerance rate of the machine which leads to an accumulation of errors. Many researchers have addressed this problem by adding certain external sources such as encoded magnetic compass, rate-gyros etc., However addition of these sensors has led to various new errors. In this paper, the use of unscented Kalman filter (UKF) is proposed along with the DR to get accurate localization information. UKF uses a deterministic sampling approach that captures the estimates of mean and covariance with a set of sigma points. The simulation results show that the proposed method is able to track the desired path with least error when compared to DR used alone. The localization of a mobile robot with the proposed system is also highly reliable.
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References
Amanatiadis, A.: A multisensor indoor localization system for biped robots operating in industrial environments. IEEE Trans. Ind. Electron. 63(12), 7597–7606 (2016)
Chen, S.Y.: Kalman filter for robot vision: a survey. IEEE Trans. Ind. Electron. 59(11), 4409–4420 (2012)
Chung, H.Y., Hou, C.C., Chen, Y.S.: Indoor intelligent mobile robot localization using fuzzy compensation and Kalman filter to fuse the data of gyroscope and magnetometer. IEEE Trans. Ind. Electron. 62(10), 6436–6447 (2015)
Constanzi, R., Fanelli, F., Monni, N., Ridolfi, A., Allotta, B.: An attitude estimation algorithm for mobile robots under unknown magnetic disturbances. IEEE/ASME Trans. Mechatron. 21(4), 1900–1911 (2016)
Faisal, M., Alsulaiman, M., Hedjar, R., Mathkour, H., Zuair, M., Altaheri, H.: ZakariahM, Bencherif MA and Mekhtiche MA.: Enhancement of mobile robot localization using extended Kalman filter. Adv. Mech. Eng. 8(11), 1–11 (2016)
Kim, J.H., Seong, P.H.: Experiments on orientation recovery and steering of an autonomous mobile robot using encoded magnetic compass disc. IEEE Trans. Instrum. Measure 45(1), 271–274 (1996)
Kim, S.J., Kim, B.K.: Dynamic ultrasonic hybrid localization system for indoor mobile robots. IEEE Trans. Ind. Electron. 60(10), 4562–4573 (2013)
Nath, T.G., Sudheesh, P., Jayakumar, M.: Tracking inbound enemy missile for interception from target aircraft using extended Kalman filter. In: Proceeding of Communications in Computer and Information Science, vol. 625, pp. 269–279. Springer (2016)
Song, K.T., Suen, Y.H.: Design and implementation of a path tracking controller with the capacity of obstacle avoidance. In: Proceeding of Automatic Control conference, pp. 134–139 (1996)
Tsai, C.C.: A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements. IEEE Trans. Instrum. Measure 47(5), 1399–1404 (1998)
Tuna, G., Gulez, K., Gungor, V.C., Mumcu, T.V.: Evaluations of different simultaneous localization and mapping (SLAM) algorithms. In: Proceeding of IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 2693–2698 (2012)
Vikranth, S., Sudheesh, P., Jayakumar, M.: Nonlinear tracking of target submarine using Extended Kalman Filter (EKF). In: Proceeding of Communications in Computer and Information Science, vol. 625, pp. 258–268 Springer (2016)
Wang, S., Chen, L., Gu, D., Hu, H.: Cooperative localization of AUVs using moving horizon estimation. IEEE/CAA J. Automatica Sinica 1(1), 68–76 (2014)
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Sudheesh, P., Jayakumar, M. (2018). Non Linear Tracking Using Unscented Kalman Filter. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_4
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DOI: https://doi.org/10.1007/978-3-319-67934-1_4
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