| Issue |
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
|
|
|---|---|---|
| Article Number | 01047 | |
| Number of page(s) | 7 | |
| Section | AI-Integrated Physics, Technology, and Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534401047 | |
| Published online | 22 December 2025 | |
https://doi.org/10.1051/epjconf/202534401047
A motion control system for a mobile floor cleaning robot using the proportional-integral-derivative method
1 Mechatronics Engineering Department, University of Trunodjoyo Madura, Bangkalan, 69162, Indonesia
2 Mechanical Engineering Department, University of Trunodjoyo Madura, Bangkalan, 69162, Indonesia
3 Industial Engineering Department, University of Trunodjoyo Madura, Bangkalan, 69162, Indonesia
4 Electrical Engineering Department, University of Trunodjoyo Madura, Bangkalan, 69162, Indonesia
* Corresponding author: asr0354@gmail.com
Published online: 22 December 2025
Floor cleaning robots are designed to perform tasks in unstructured environments based on human-provided logic. Many commercially available robots use inefficient random methods, creating a need for a more precise navigation and motion control system. The research contribution is the practical implementation and evaluation of a hybrid motion control system using the Proportional-Integral-Derivative (PID) method. This system integrates feedback from rotary encoders and a camera to minimize movement errors and overcome odometry drift. The robot maps a 200cm x 200cm arena. It combines a rotary encoder to measure distance traveled and a camera to detect visual markers for positioning and direction. The PID algorithm processes this fused sensor data to correct the robot’s path. The research successfully determined the optimal PID parameters to be Kp = 1, Ki = 0.07, and Kd = 0.08. With these parameters, the system effectively reduced the average error rate to 6.126% across three tests, demonstrating stable and accurate movement from the start to the final position.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.