Abstract
Robot localization is vital for the operation of an automated guided vehicle (AGV) but is susceptible to problems such as wheel slip. With more sensors fused together, the more environmental information can be collected by the AGV, which helps with the localization of AGV. Inertial measurement unit (IMU) and global positioning unit (GPS) are usually implemented to improve robot localization but are susceptible to noise and are effective outdoors. Indoors, however, are more suitable with light detection and ranging (lidar) device. This paper implements extended Kalman filter (EKF) and unscented Kalman filter (UKF) for robot localization on AGV. AGV localization was tested with EKF and UKF on three different test tracks with different turn conditions. The performance of the EKF and UKF was compared to each other. Different sensors were implemented along with sensor fusion. UKF generates better odometry estimation than EKF with 24.07% better accuracy. With the usage of lidar, wheel slip was compensated.
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References
M.B. Alatise, G.P. Hancke, A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access 8, 39830–39846 (2020). https://doi.org/10.1109/access.2020.2975643
Y. Qingmei, S. Jianmin, A data fusion method applied in an autonomous robot (2008)
S. Jusoh, S. Almajali, A systematic review on fusion techniques and approaches used in applications. IEEE Access 8, 14424–14439 (2020). https://doi.org/10.1109/ACCESS.2020.2966400
A. Eman, H. Ramdane, Mobile robot localization using extended Kalman filter, in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 19–21 Mar 2020, pp. 1–5
M. Thirumarimurugan, N. Bagyalakshmi, P. Paarkavi, Comparison of fault detection and isolation methods: a review, in 2016 10th International Conference on Intelligent Systems and Control (ISCO), 7–8 Jan 2016, pp. 1–6
H. Deilamsalehy, T.C. Havens, Sensor fused three-dimensional localization using IMU, camera and LiDAR, in 2016 IEEE SENSORS, 30 Oct–3 Nov 2016, pp. 1–3
M.L. Anjum, J. Park, W. Hwang, H. Kwon, J. Kim, C. Lee, K. Kim, D.D. Cho, Sensor data fusion using unscented Kalman filter for accurate localization of mobile robots, in ICCAS 2010, 27–30 Oct 2010, pp. 947–952
M. Dares, K.W. Goh, Y.S. Koh, C.F. Yeong, E. Su, P.H. Tan, Development of AGV as test bed for fault detection, in 2020 6th International Conference on Control, Automation and Robotics (ICCAR), 20–23 Apr 2020, pp. 379–383
P. Yazdjerdi, N. Meskin, Actuator fault detection and isolation of differential drive mobile robots using multiple model algorithm, in 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), 5–7 Apr 2017, pp. 0439–0443
G. Ziwei, L. Rong, 2D range flow-based odometry fusing LiDAR and IMU, in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 6–8 Dec 2019, pp. 2761–2765
Acknowledgements
This work is supported by Universiti Teknologi Malaysia Research grant [01M85, 4B402] and Collaborative Research in Engineering, Science and Technology center (CREST) R&D grant [T20C2-18]. This work is also a collaboration with DF Automation Sdn. Bhd.
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Dares, M., Goh, K.W., Koh, Y.S., Yeong, C.F., Su, E.L.M., Tan, P.H. (2022). Automated Guided Vehicle Robot Localization with Sensor Fusion. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_11
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DOI: https://doi.org/10.1007/978-981-16-8484-5_11
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