Precise Heading Estimation of Physically Connected off-Road Robotized Vehicles in the RoboShepherd System

XVII International Conference on Systems, Automatic Control and Measurements, SAUM 2024 (pp. 145-148)

АУТОР(И) / AUTHOR(S): Miša Tomić , Miloš Simonović , Miloš Milošević ,Vukašin Pavlović

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DOI:  10.46793/SAUM24.145T

САЖЕТАК / ABSTRACT:

Areas of application for various types of robots are now diverse, ranging from healthcare, support for the elderly or surgery, to autonomous and driverless vehicles (wheeled or flying) or assistance with driving. Recently, there has been increased interest in the use of robots in gardening and agriculture. Robots are increasingly being used in groups rather than alone to perform tasks as they become more commonplace in our daily lives. Swarm robots, i.e. groupings of self-organised, autonomous, cooperative and coordinated robots, are one of the fastest growing topics in robotics. A group of off-road robotized vehicles, where each robot is connected by a wire to the two adjacent robots forming a closed polygon, can form a kind of movable fence. This system can be used as an autonomous system for herd keeping and breeding – RoboShepherd. In order to achieve precise navigation of this complex system, on a non-flat slippery surface, precise heading of each robotized vehicle is crucial. This paper presents a method used to accurately estimate the heading of each robotic vehicle in the RoboShepherd system.

КЉУЧНЕ РЕЧИ / KEYWORDS:

robot, heading, navigation, RoboShepherd

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This paper is part of the project ‘ RoboShepherd’ realized at the University of Niš, Faculty of Mechnical engineering, and Coming – Computer Engineering, Belgrade, which was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, trough Innovation Fund.

ЛИТЕРАТУРА / REFERENCES:

  1. Dietz, Griffin, Jane L. E, Peter Washington, Lawrence H. Kim, and Sean Follmer. „Human perception of swarm robot motion.“ In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems, pp. 2520-2527. 2017.
  2. Diep, Quoc Bao, Ivan Zelinka, and Roman Senkerik. „An algorithm for swarm robot to avoid multiple dynamic obstacles and to catch the moving target.“ In Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings, Part II 18, pp. 666-675. Springer International Publishing, 2019.
  3. Suzuki, Ryo, Clement Zheng, Yasuaki Kakehi, Tom Yeh, Ellen Yi-Luen Do, Mark D. Gross, and Daniel Leithinger. „Shapebots: Shape-changing swarm robots.“ In Proceedings of the 32nd annual ACM symposium on user interface software and technology, pp. 493-505. 2019.
  4. Schmickl, Thomas, and Karl Crailsheim. „Trophallaxis among swarm-robots: A biologically inspired strategy for swarm robotics.“ In The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006., pp. 377-382. IEEE, 2006.
  5. Zhao, Haitao, Hai Liu, Yiu-Wing Leung, and Xiaowen Chu. „Self-adaptive collective motion of swarm robots.“ IEEE Transactions on Automation Science and Engineering 15, no. 4 (2018): 1533-1545.
  6. Baldassarre, Gianluca, Vito Trianni, Michael Bonani, Francesco Mondada, Marco Dorigo, and Stefano Nolfi. „Self-organized coordinated motion in groups of physically connected robots.“ IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 37, no. 1 (2007): 224-239.
  7. Baldassarre, Gianluca, Stefano Nolfi, and Domenico Parisi. „Evolution of collective behavior in a team of physically linked robots.“ In Workshops on Applications of Evolutionary Computation, pp. 581-592. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
  8. Rahman, Md Mostafizar, and Kazunobu Ishii. „Heading estimation of robot combine harvesters during turning maneuveres.“ Sensors 18, no. 5 (2018): 1390.
  9. Haider, Muhammad Husnain, Zhonglai Wang, Abdullah Aman Khan, Hub Ali, Hao Zheng, Shaban Usman, Rajesh Kumar, M. Usman Maqbool Bhutta, and Pengpeng Zhi. „Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion.“ Journal of King Saud University-Computer and Information Sciences 34, no. 10 (2022): 9060-9070.
  10. Al-Faiz, Mohammed Z., and Ghufran E. Mahameda. „GPS-based navigated autonomous robot.“ International Journal of Emerging Trends in Engineering Research 3, no. 4 (2015).
  11. Leanza, Antonio, Rocco Galati, Angelo Ugenti, Eugenio Cavallo, and Giulio Reina. „Where am I heading? A robust approach for orientation estimation of autonomous agricultural robots.“ Computers and Electronics in Agriculture 210 (2023): 107888.
  12. Tran, Tien-Dung Quoc, and Vinh-Hao Nguyen. „Heading Estimation for Autonomous Robot Using Dual-Antenna GPS.“ International Journal of Mechanical Engineering and Robotics Research 9, no. 12 (2020): 1566-1572.