Abstract
Designing membrane controllers for single- and multi-robot systems is an application area initiated in 2011 at the Laboratory of Natural Computing and Robotics (natuRO) of the Politehnica University of Bucharest. In this paper, an overview of natuRO’s research on the design of robot controllers based on various models of membrane systems is given. After an introduction to robotics and natural computing, this paper follows multiple directions. Firstly, a description of three membrane system simulators is given, taking into account their evolution, comparative capabilities, and application areas for each of them. Secondly, the main part of this paper is a synopsis of the applications of membrane systems in robot control, while an emphasis is paid to the new membrane computing models introduced at natuRO, Enzymatic Numerical P Systems and XP colonies, and their specific use in single- and multiple-robot applications. Thirdly, the paper continues with a critical overview of the performances of membrane controllers as compared to traditional ways to control single- and multiple-robot systems, current challenges and possible ways to overcome these. Example avenues for future related works are given in the conclusion.
Similar content being viewed by others
References
Abiyev, R. H., Günsel, I., Akkaya, N., Aytac, E., Çağman, A., & Abizada, S. (2016). Robot soccer control using behaviour trees and fuzzy logic. Procedia Computer Science, 102, 477–484. https://doi.org/10.1016/J.PROCS.2016.09.430.
Arsene, O., Buiu, C., & Popescu, N. (2011). SNUPS—A simulator for numerical membrane computing. International Journal of Innovative Computing, 7(6), 3509–3522.
Assis, L., Soares, A. D. S., Coelho, C. J., & Van Baalen, J. (2016). An evolutionary algorithm for autonomous robot navigation. Procedia Computer Science, 80, 2261–2265. https://doi.org/10.1016/J.PROCS.2016.05.404.
Buiu, C., & Gansari, M. (2014). A New Model for Interactions Between Robots in a Swarm. In: Electronics, Computers and Artificial Intelligence (ECAI), 2014 6th International Conference On. pp. 5–10 (Oct 2014)
Buiu, C., Vasile, C., & Arsene, O. (2012). Development of membrane controllers for mobile robots. Information Sciences, 187(1), 33–51. https://doi.org/10.1016/j.ins.2011.10.007.
Bustos, P., Manso, L., Bandera, A., Bandera, J., García-Varea, I., & Martínez-Gómez, J. (2019). The CORTEX cognitive robotics architecture: Use cases. Cognitive Systems Research, 55, 107–123. https://doi.org/10.1016/J.COGSYS.2019.01.003.
de Castro, L. N. (2007). Fundamentals of natural computing: An overview. Physics of Life Reviews, 4(1), 1–36. https://doi.org/10.1016/J.PLREV.2006.10.002.
Fang, W., Chao, F., Yang, L., Lin, C. M., Shang, C., Zhou, C., et al. (2019). A recurrent emotional CMAC neural network controller for vision-based mobile robots. Neurocomputing, 334, 227–238. https://doi.org/10.1016/J.NEUCOM.2019.01.032.
Florea, A. G. (2018). Contributions to the Control of Collective Robotic Systems Using Membrane Computing. Phd thesis, Politehnica University of Bucharest, Bucharest, Romania.
Florea, A. G., & Buiu, C. (2016). Synchronized dispersion of robotic swarms using XP colonies. In: Electronics, Computers and Artificial Intelligence (ECAI), 2016 8th International Conference On. pp. 1–6. IEEE. https://doi.org/10.1109/ECAI.2016.7861107
Florea, A. G., & Buiu, C. (2017). Membrane computing for distributed control of robotic swarms: Emerging research and opportunities. IGI Global, USA. https://doi.org/10.4018/978-1-5225-2280-5,
Florea, A. G., & Buiu, C. (2017). Modelling multi-robot interactions using a generic controller based on numerical P systems and ROS. In: Electronics, Computers and Artificial Intelligence (ECAI), 2017 9th Edition International Conference On. p. in press. https://doi.org/10.1109/ECAI.2017.8166411.
Florea, A. G., & Buiu, C. (2019). Sensor fusion for autonomous drone waypoint navigation using ROS and numerical P systems: A critical analysis of its advantages and limitations. In: The 22nd international conference on control systems and computer science. p. accepted for publication. Bucharest, Romania.
Florea, A. G., & Buiu, C. (2016). Development of a software simulator for P colonies. Applications in robotics. International Journal of Unconventional Computing, 12(2–3), 189–205.
Florea, A. G., & Buiu, C. (2018). A distributed approach to the control of multi-robot systems using XP colonies. Integrated Computer-Aided Engineering, 25(1), 15–29. https://doi.org/10.3233/ICA-170554.
Haiek, D. E., Aboulissane, B., Bakkali, L. E., & Bahaoui, J. E. (2019). Optimal trajectory planning for spherical robot using evolutionary algorithms. Procedia Manufacturing, 32, 960–968. https://doi.org/10.1016/J.PROMFG.2019.02.309.
Hawkes, E. W., Blumenschein, L. H., Greer, J. D., & Okamura, A. M. (2017). A soft robot that navigates its environment through growth. Science Robotics, 2(8), eaan3028. https://doi.org/10.1126/scirobotics.aan3028.
Heylighen, F. (2016). Stigmergy as a universal coordination mechanism I: Definition and components. Cognitive Systems Research, 38, 4–13.
Larsen, L., Schuster, A., Kim, J., & Kupke, M. (2018). Path planning of cooperating industrial robots using evolutionary algorithms. Procedia Manufacturing, 17, 286–293. https://doi.org/10.1016/J.PROMFG.2018.10.048.
Laschi, C., Mazzolai, B., & Cianchetti, M. (2016). Soft robotics: Technologies and systems pushing the boundaries of robot abilities. Science Robotics, 1(1), eaah3690. https://doi.org/10.1126/scirobotics.aah3690.
Luan, F., Na, J., Huang, Y., & Gao, G. (2019). Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence. Neurocomputing, 337, 153–164. https://doi.org/10.1016/J.NEUCOM.2019.01.063.
Masmoudi, M. S., Krichen, N., Masmoudi, M., & Derbel, N. (2016). Fuzzy logic controllers design for omnidirectional mobile robot navigation. Applied Soft Computing, 49, 901–919. https://doi.org/10.1016/J.ASOC.2016.08.057.
Mohanta, J., & Keshari, A. (2019). A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation. Applied Soft Computing, 79, 391–409. https://doi.org/10.1016/J.ASOC.2019.03.055.
Orozco-Rosas, U., Montiel, O., & Sepúlveda, R. (2019). Mobile robot path planning using membrane evolutionary artificial potential field. Applied Soft Computing, 77, 236–251. https://doi.org/10.1016/J.ASOC.2019.01.036.
Pavel, A. B., & Buiu, C. (2011). Using enzymatic numerical P systems for modeling mobile robot controllers. Natural Computing, 11(3), 387–393.
Pérez-Hurtado, I., Pérez-Jiménez, M. J., Orellana-Martín, D., & Zhang, G. (2018). Simulation of rapidly-exploring random trees in membrane computing with P-lingua and automatic programming. International Journal of Computers, Communications and Control, 13(6), 1007–1031. https://doi.org/10.15837/ijccc.2018.6.3370.
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A.Y. (2009). ROS: An Open-Source Robot Operating System. In: ICRA Workshop on Open Source Software. vol. 3, p. 5. Kobe.
Rubenstein, M., Ahler, C., & Nagpal, R. (May 2012). Kilobot: A Low Cost Scalable Robot System for Collective Behaviors. In: 2012 IEEE International Conference on Robotics and Automation (ICRA). pp. 3293–3298. Ieee. https://doi.org/10.1109/ICRA.2012.6224638.
SPARC, EuRobotics: Robotics Multi-Annual Roadmap. https://www.eu-robotics.net/sparc/about/roadmap/index.html).
Vasile, C. I. (2015). Distributed Control for Multi-Robot Systems. Phd, Politehnica University of Bucharest.
Vega-Heredia, M., Mohan, R. E., Wen, T. Y., Aisyah, J. S., Vengadesh, A., Ghanta, S., et al. (2019). Design and modelling of a modular window cleaning robot. Automation in Construction, 103, 268–278. https://doi.org/10.1016/J.AUTCON.2019.01.025.
Verl, A., Valente, A., Melkote, S., Brecher, C., Ozturk, E., & Tunc, L. T. (2019). Robots in machining. CIRP Annals,. https://doi.org/10.1016/J.CIRP.2019.05.009.
Wang, X., Zhang, G., Neri, F., Jiang, T., Zhao, J., Gheorghe, M., et al. (2015). Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots. Integrated Computer-Aided Engineering, 23(1), 15–30. 10.3233/ICA-150503.
Wang, X. Y., Zhang, G. X., Zhao, J. B., Rong, H. N., Ipate, F., & Lefticaru, R. (2015). A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning. International Journal of Computers, Communications and Control, 10(5), 732–745. 10.15837/ijccc.2015.5.2030.
Williams, H. A., Jones, M. H., Nejati, M., Seabright, M. J., Bell, J., Penhall, N. D., et al. (2019). Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 181, 140–156. https://doi.org/10.1016/J.BIOSYSTEMSENG.2019.03.007.
Yang, G. Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., et al. (2018). The grand challenges of science robotics. Science Robotics, 3(14), eaar7650. https://doi.org/10.1126/scirobotics.aar7650.
Zhang, L., Merrifield, R., Deguet, A., & Yang, G. Z. (2017). Powering the world’s robots—10 years of ROS. Science Robotics, 2(11), eaar1868. https://doi.org/10.1126/scirobotics.aar1868.
Zhang, G., Pérez-Jiménez, M. J., & Gheorghe, M. (2017). Real-life applications with membrane computing, emergence, complexity and computation (Vol. 25). Cham: Springer. https://doi.org/10.1007/978-3-319-55989-6.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Buiu, C., Florea, A.G. Membrane computing models and robot controller design, current results and challenges. J Membr Comput 1, 262–269 (2019). https://doi.org/10.1007/s41965-019-00029-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41965-019-00029-8