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Heading stabilization of a mecanum wheel mobile robot using Kalman filter and SMC under variation condition

Ardianto Syaifur Rohman, Tunjung Genarsih, Sihmaulana Dwianto, Nuzula Afianah, Salsabila Liandra Putri, Nurul Zainal Fanani, Mochamad Irwan Nari, Ahmad Rofi’i, Fendik Eko Purnomo, Syamsiar Kautsar
July 19, 2026
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Research Abstract & Technology Focus

This paper presents a robust heading stabilization system for a mecanum wheel mobile robot by integrating a Kalman filter (KF) with sliding mode control (SMC). A two-state KF estimates the robot’s heading angle and gyroscope bias from ICM20948 inertial measurement unit (IMU) measurements, reducing sensor noise by 65% and compensating for bias drift of 0.3° per second. The estimated heading is regulated using SMC with a boundary layer to minimize chattering. Implemented on a Raspberry Pi 3B, the system was validated under varying surface friction conditions and external disturbances. The controller achieved heading stabilization with root mean square error (RMSE) between 0.380° and 0.589° across all surfaces, steady-state error within ±0.5°, and convergence within 2.0–2.6 seconds. Under severe disturbances causing heading deviations up to 238°, rapid recovery within 0.5 seconds was achieved with only 3.04° final steady state error. The results demonstrate the feasibility of implementing robust heading stabilization on low-cost embedded platforms for autonomous navigation applications.
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What is the core focus of the research titled 'Heading stabilization of a mecanum wheel mobile robot using Kalman filter and SMC under variation condition'?

This literature focuses on: This paper presents a robust heading stabilization system for a mecanum wheel mobile robot by integrating a Kalman filter (KF) with sliding mode control (SMC). A two-state KF estimates the robot’s heading angle and gyroscope bias from ICM20948 ine...

What other academic literature is closely related to 'Heading stabilization of a mecanum wheel mobile robot using Kalman filter and SMC under variation condition'?

Yes, highly correlated activity was mapped. An entry titled 'Enhanced Trajectory Tracking Accuracy of a Mobile Manipulator via MRE Intelligent Isolation System Under Continuous Impact Disturbances' discusses this: Continuous impact vibrations caused by uneven road surfaces (such as speed bumps) can significantly reduce the trajectory tracking accuracy of mobi...

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