Tracking Objects with Mecanum Wheel Robot Cars using Computer Vision

Aan Darmawan Hangkawidjaja; Ratnadewi Ratnadewi; Agus Prijono; Dasapta Erwin Irawan; Satya Wibowo

doi:10.35877/454RI.asci4809

Authors

Aan Darmawan Hangkawidjaja Universitas Kristen Maranatha, Bandung, Indonesia
Ratnadewi Ratnadewi Universitas Kristen Maranatha, Bandung, Indonesia
Agus Prijono Universitas Kristen Maranatha, Bandung, Indonesia
Dasapta Erwin Irawan Institut Teknologi Bandung, Bandung, Indonesia
Satya Wibowo Universitas Kristen Maranatha, Bandung, Indonesia

DOI:

https://doi.org/10.35877/454RI.asci4809

Keywords:

Mechanum Wheel, Computer Vision, HSV

Abstract

The purpose of this study is to describe the development of a mechanical wheeled robot car (using Mecanum wheels) that can follow an object via Computer Vision (CV). The object tracked is a regular 2-dimensional shape with a specific color (yellow), which can be attached to a person or moving object and followed by the robot at a set distance. The research methodology uses thr CV process, which begins by identifying the color of the object in the HSV (Hue, Saturation, and Value) color space, followed by shape recognition based on the number of contour keypoints obtained through Structural Analysis and Shape Descriptors. The outcomes of this combination enable the recognition of objects with a specific shape and color. To maintain the distance between the object and the robot, a calibration procedure is performed, and the distance between the robot's camera and the object is calculated using mathematical 2D distance calculations. This robot was built with a Single Board Computer (SBC) Raspberry Pi 4B+ controller (4GB memory and 32GB external memory), a Pi camera as the vision sensor, a Debian 11.0 "Bullseye" operating system, and a Python application, with assistance from the OpenCV library. According to test results on a yellow triangle-shaped object, the robot is able to track the movement of the object effectively, as intended, despite interference from objects with triangular shapes of different colors or objects of the same color.

Downloads

Download data is not yet available.

References

Abdullah-Al-Noman, M., Eva, A. N., Yeahyea, T. B., & Khan, R. (2022). Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection. Journal of Robotics and Control (JRC), 3(2), 180–186. https://doi.org/10.18196/jrc.v3i2.13906

Algabri, R., & Choi, M.-T. (2020). Deep-learning-based Indoor Human Following of Mobile Robot Using Color Feature. Sensors, 20(2699), 19.

Amorim, T. G. S., Souto, L. A., P. Do Nascimento, T., & Saska, M. (2021). Multi-Robot Sensor Fusion Target Tracking with Observation Constraints. IEEE Access, 9, 52557–52568. https://doi.org/10.1109/ACCESS.2021.3070180

Das, P., Biswas, A., Pal, A., Das, R., Mahato, D. R., Mondal, J. P., Biswas, J., Saha, A., & Ghosh, A. (2022). Human Following Robot. International Journal of Creative Research Thoughts (IJCRT), 10(3), 174–185. https://doi.org/10.54389/yefj9778

Fitrawan, A. A., Shodiq, M. N., Kusuma, D. H., & Jannah, F. R. U. (2020). Sistem Pendeteksi Jarak Pada Objek Realtime Video Berdasarkan Luas Kontur Menggunakan Metode Circle Hough. Seminar Nasional Terapan Riset Inovatif (SENTRINOV), 6(1), 935–942.

Gad, A., Basmaji, T., Yaghi, M., Alheeh, H., Alkhedher, M., & Ghazal, M. (2022). Multiple Object Tracking in Robotic Applications: Trends and Challenges. Applied Sciences (Switzerland), 12(19). https://doi.org/10.3390/app12199408

Masiado, S. L., Constantino, A. D., Cajurao, J. F., Valenciano, J. V., Lagayao, J. V., Dasmariñas, J. S. S., & Laud, R. J. S. (2025). GrainBot: An Android-Controlled Rice Grain Collector Robot. International Journal of Advanced Science Computing and Engineering, 7(2), 60–70. https://doi.org/10.62527/ijasce.7.2.258

OpenCV: Structural Analysis and Shape Descriptors. (2009). https://doi.org/10.1007/978-3-031-02186-2_4

Patoliya, J. J., & Mewada, H. K. (2019). Automated tracking and real time following of moving person for robotics applications. Journal of Automation, Mobile Robotics and Intelligent Systems, 13(4), 31–37. https://doi.org/10.14313/JAMRIS/4-2019/35

Puri, R., Gupta, A., & Sikri, M. (2018). Contour, Shape, and Color Detection Using Open CV-Python. International Journal of Advances in Electronics and Computer Science, 5(3), 2393–2835.

Sugiarto, K., Giyantara, A., & Yulisya, R. D. (2022). Object Tracking Dengan Menggunakan Color Filtering HSV Pada Robot World Cup. Jurnal Bumigora Information Technology (BITe), 4(2), 143–152. https://doi.org/10.30812/bite.v4i2.2156

Tsai, T. H., & Yao, C. H. (2020). A robust tracking algorithm for a human-following mobile robot. IET Image Processing, 15(3), 786–796. https://doi.org/10.1049/ipr2.12062

Tuya. (2024). HSV and RGB-TuyaOS-Tuya Developer. Tuya Inc.

Wahrini, R. (2023). Development of Color-Based Object Follower Robot Using Pixy 2 Camera and Arduino to Support Robotics Practice Learning. Jurnal Edukasi Elektro, 7(2), 161–168. https://doi.org/10.21831/jee.v7i2.64413

Williams, A. (2022). All about Mecanum|Hackaday.

Yevsieiev, V., Abu-Jassar, A., & Maksymova, S. (2023). Object Recognition and Tracking Method in the Mobile Robor’s Workspace in Real Time. Technical Science Research in Uzbekistan, 2(2), 356–363.

Zanchettin, A. M. (2023). 2023 Andrea Mariag Zanchettin Human tracking from quantised sensors_ ...tion to safe human–robot collaboration.pdf. Control Engineering Practice, 141(105727). https://doi.org/https://doi.org/10.1016/j.conengprac.2023.105727