Abstract
This paper compares the performance of four different controllers implemented on two multi agent robots to stabilize its motion from one station to another during delivery tasks. The controllers are; multi-position controller, PID controller, fuzzy logic controller and fuzzy-PID controller. The aim of this paper is to control the mobile robot robustly to arrive its target destination. The robots and station coordinates are recognized using machine vision system and all programming is carried out in LabVIEW. The paper compares the transient response and steady state error of each of controller and experimental results show that the Fuzzy-PID controller produced the best performance and good trajectory of robot from its current position to its target position. It had a better convergence rate when compared with other controllers like PID and Fuzzy logic controllers.
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References
Abadi, D.N., Khooban, M.H.: Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots. J. King Saud Univ. – Eng. Sci. 27(1), 92–100 (2015)
Abatari, H.T., Tafti, A.D.: Using a fuzzy PID controller for the path following of a car-like mobile robot. In: 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran, 13–15 February 2013
Azar, A.T., Serrano, F.E.: Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications. Neural Comput. Appl. 25(5), 983–995 (2014). https://doi.org/10.1007/s00521-014-1560-x
Azar, A.T., Serrano, F.E.: Design and modeling of anti wind up PID controllers. In: Zhu, Q., Azar, A. (eds.) Complex System Modelling and Control Through Intelligent Soft Computations. STUD FUZZ, vol. 319, pp. 1–44. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-12883-2_1
Erden, M.S., Leblebicioglu, K., Halici, U.: Multi-agent system-based fuzzy controller design with genetic tuning for a mobile manipulator robot in the hand over task. J. Intell. Rob. Syst. 39(4), 287–306 (2004)
Finžgar, M., Podržaj, P.: Machine-vision-based human-oriented mobile robots: a review. J. Mech. Eng. 63(5), 331–348 (2017)
Iñigo-Blasco, P., Diaz-del-Rio, F., Romero-Ternero, M.C., Cagigas-Muñiz, D., Vicente-Diaz, S.: Robotics software frameworks for multi-agent robotic systems development. Robot. Auton. Syst. 60(6), 803–821 (2012)
Mavridis, N.: A review of verbal and non-verbal human–robot interactive communication. Robot. Auton. Syst. 63, 22–35 (2015)
Mizoguchi, F., Nishiyama, H., Ohwada, H., Hiraishi, H.: Smart office robot collaboration based on multi-agent programming. Artif. Intell. 114(1–2), 57–94 (2008)
Parker, L.E.: Current research in multi robot systems. Artif. Life Robot. 7(1–2), 1–5 (2003)
Pérez, L., Rodríguez, Í., Rodríguez, N., Usamentiaga, R., García, D.F.: Robot guidance using machine vision technique. Sensors 16(3), 335 (2016)
Prusty, S.B., Pati, U.C., Mahapatra, K.: Implementation of Fuzzy-PID controller to liquid level system using LabVIEW. In: Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), Calcutta, pp. 36–40 (2014)
Rossomando, F.G., Soria, C.M.: Identification and control of nonlinear dynamics of a mobile robot in discrete time using an adaptive technique based on neural PID. Neural Comput. Appl. 26(5), 1179–1191 (2015)
Soldan, S., Welle, J., Barz, T., Kroll, A., Schulz, D.: Towards autonomous robotic systems for remote gas leak detection and localization in industrial environments. In: Yoshida, K., Tadokoro, S. (eds.) Field and Service Robotics. STAR, vol. 92, pp. 233–247. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-40686-7_16
Suster, P., Jadlovska, A.: Tracking trajectory of the mobile robot Khepera II using approaches of artificial intelligence. Acta Electrotechnica et Informatica 11(1), 38–43 (2011)
Su, X., Wang, C., Su, W., Ding, Y.: Control of balancing mobile robot on a ball with fuzzy self-adjusting PID. In: 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, pp. 5258–5262 (2016)
Vinod, R.N., Mathew, A.T.: Design, simulation and implementation of cascaded path tracking controller for a differential drive mobile robot. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, pp. 1085–1090 (2015)
Vittorio, P., Manuela, V.: Handling complex commands as service robot task requests. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, pp. 117–118, 25–31 July 2015
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
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Ammar, H.H., Azar, A.T., Tembi, T.D., Tony, K., Sosa, A. (2018). Design and Implementation of Fuzzy PID Controller into Multi Agent Smart Library System Prototype. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_13
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