Design of Behavior-Based Reactive System for Autonomous Stuff-Collecting Mobile Robot
Keywords:behavior-based, reactive, autonomous, negotiator, mobile robot
Recently, autonomous robots play an important role in many aspects. Autonomous robots help improve efficiency and productivity, further reduce errors, and risk rates. Employee safety in high-risk work environments is also becoming one of the benefits of autonomous robots. When designing an autonomous robot, two paradigms can be implemented, i.e., planning and reactive paradigm. A distinct feature of the reactive paradigm is that all activities are carried out through behavior. The benefits of the reactive paradigm include can be applied in limited inexpensive hardware resources, low complexity, goal convergence, easy adaptation to changing situations, and a completely unfamiliar environment with unpredictable mobile obstacles. In this paper, the behavior-based reactive system of an autonomous mobile robot is designed to collect stuff objects. Negotiators have been proposed to combine several behaviors into a system that can be implemented in a variety of situations and cases. The results show that a negotiator can be implemented to realize a fully autonomous mobile robot based on the behavior of the reactive system.
J. R. S. Ibáñez, C. Perez-del-Pulgar, A. Garcia, “Path planning for autonomous mobile robots: A review,” Sensors, vol. 21, h. 7898, 2021. Doi: 10.3390/s21237898.
M. Robin. (2000). Introduction to AI Robotics. MIT Press.
X. Wang, J. Zhang, “RPL: A robot programming language based on reactive agent,” 2017. Doi: 10.2991/eame-17.2017.59.
T. A. Tobaruela, A. Rodríguez, “Reactive navigation in extremely dense and highly intricate environments,”. PLOS ONE, vol. 12, e0189008, 2017. Doi: 10.1371/journal.pone.0189008.
I. Hassani, I. Maalej, C. Rekik, “Robot path planning with avoiding obstacles in known environment using free segments and turning points algorithm,” Mathematical Problems in Engineering, h. 1-13, 2018. Doi: 10.1155/2018/2163278.
N. Lazzeri, D. Mazzei, L. Cominelli, A. Cisternino, D.D. Rossi, “Designing the mind of a social robot,” Applied Sciences, vol. 8, h. 302, 2018. Doi: 10.3390/app8020302.
J. Savage, S. Muñoz, L. Contreras, M. Matamoros, M. Negrete, C. Rivera, G. Steinbabuer, O. Fuentes, H. Okada, “Generating reactive robots’ behaviors using genetic algorithms,” h. 698-707, 2021. Doi: 10.5220/0010229306980707.
M. Alatise, G. Hancke, “A review on challenges of autonomous mobile robot and sensor fusion methods,” IEEE Access, h. 1-1, 2020. Doi: 10.1109/ACCESS.2020.2975643.
T. Asokan, “Autonomy for robots: design and developmental challenges (Keynote Address),” Procedia Technology, vol. 23, h. 4-6, 2016. Doi: 10.1016/j.protcy.2016.03.066.
P. Panigrahi, S. Bisoy, “Localization strategies for autonomous mobile robots: a review,” Journal of King Saud University-Computer and Information Sciences, 2021. Doi: 10.1016/j.jksuci.2021.02.015.
J.-H. Cho, Y.-T. Kim, “Design of autonomous logistics transportation robot system with fork-type lifter,” International Journal of Fuzzy Logic and Intelligent Systems, vol. 17, h. 177-186, 2017. Doi: 10.5391/IJFIS.2017.17.3.177.
F. Giuseppe, D. Koster, R. Sgarbossa, F. Strandhagen, and J. Ola, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” European Journal of Operational Research, vol. 294, 2021. Doi: 10.1016/j.ejor.2021.01.019.
How to Cite
Copyright (c) 2022 Eddy Wijanto
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.