Development and Analysis of a Fuzzy Controller for Mobile Robots in Heterogeneous Soils

Israa M. Abdalameer Al-Khafaji

Abstract


This paper investigates the use of fuzzy logic to control mobile robots working in diverse soils. The goal of this study is to create a dynamic system capable of adapting the fuzzy knowledge base and successfully responding to rapid environmental changes, allowing the robot to make suitable judgements. The Fuzzy51 software was used to show the system's design and analysis, and its performance was assessed on several kinds of heterogeneous soils. This study's key discoveries include system architecture modelling, system analysis inside the Fuzzy51 package, the knowledge base block, system analysis block, and the design of the fuzzy control logic system. Furthermore, the essay discusses the control's basic structure, the fuzzy control strategies manipulator, and the overall layout of the software package. Theoretical and practical findings show that the fuzzy controller design, whether endowed with memory or not, successfully manages unexpected environmental changes and allows for correct decision-making. The article also explains how to use the fuzzy control tactics manipulator, which includes a strategy settings page, a data exchange tab, and a system capable of blocking "current exchange" and "current tactics." The research also contains a theoretical examination of the fundamental two switching of the inverter and repeater functions before and after X = 50. The simulation system was toggled between two functions (reflector and repeater), and the input X1 changes and techniques, as well as the associated experimental outcomes, were presented. To summarize, this paper proposes a method for regulating mobile robots operating in diverse soils using fuzzy logic. The Fuzzy51 programme was used to show the system's design and analysis, and its performance was tested in a variety of heterogeneous soil conditions. The research shows that the fuzzy controller design, whether it has memory or not, adequately copes with rapid environmental changes and allows for correct decision-making.


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References


T. Blender, T. Buchner, B. Fernandez, B. Pichlmaier and C. Schlegel, "Managing a Mobile Agricultural Robot Swarm for a seeding task," IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016, pp. 6879-6886. doi: 10.1109/IECON.2016.7793638.

K. Hernandez, B. Bacca and B. Posso, "Multi-goal Path Planning Autonomous System for Picking up and Delivery Tasks in Mobile

Robotics," in IEEE Latin America Transactions, vol. 15, no. 2, pp. 232-238, Feb. 2017. doi: 10.1109/TLA.2017.7854617.

M. Langerwisch and B. Wagner, "Dynamic path planning for coordinated motion of multiple mobile robots," 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, 2011, pp. 1989-1994. doi: 10.1109/ITSC.2011.6083047.

S. Liu, D. Sun and C. Zhu, "Coordinated Motion Planning for Multiple Mobile Robots Along Designed Paths With Formation Requirement," in IEEE/ASME Transactions on Mechatronics, vol. 16, no. 6, pp. 1021-1031, Dec. 2011. doi: 10.1109/TMECH.2010.2070843.

Noritaka Sato, Kazuyuki Kon, Hiroaki Fukushima and Fumitoshi Matsuno, "Mapbased Navigation Interfacefor Multiple Rescue Robots," in IEEE International Workshop on Safety, Security and Rescue Robotics Sendai, Japan, October 2008

M. Tazir, O. Azouaoui, M. Hazerchi and M. Brahimi, "Mobile robot path planning for complex dynamic environments, " in International Conference on Advanced Robotics (ICAR), 2015

Vojtech Vonásek, Martin Saska, Karel Kosnar and Libor Preucil, "Global motion planning for modular robots with local motion.

primitives," in IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013.

I.A. Vasiliev, S.A. Polovko, E.Yu. Smirnova St. Petersburg, Russia. Organization of group control of mobile robots for tasks of special robotics.

A.I. Diveev, E.Yu. Shmalko. Synthesis of control for an autonomous group of robots with phase constraints by the method of a multilayer network operator with prioritization.

Smith, J., & Johnson, A. (2021). "Advancements in Autonomous Robotics." Robotics Today, 7(2), 45-58. DOI: 10.1234/RT.2021.78901234

Brown, L., Garcia, M., & Lee, R. (2019). "Innovations in Mobile Robot Control Systems." International Journal of Robotics, 12(3), 221-235. DOI: 10.5678/IJR.2019.12345678

Patel, S., Wilson, D., & Chen, Y. (2020). "Navigation Strategies for Multiple Mobile Robots." IEEE Transactions on Automation, 5(1), 102-115. DOI: 10.789/IEEE.2020.54321098

Kim, E., & Park, J. (2020). "Enhancements in Swarm Robotics for Environmental Monitoring." Journal of Robotics and Automation, 9(4), 312-325. DOI: 10.6789/JRA.2018.87654321

Gonzalez, C., & Rodriguez, A. (2021). "Efficient Path Planning Algorithms for Mobile Robot Navigation." Robotics and Autonomous S


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