Abstract
Numerical and Enzymatic Numerical P systems are used to design mobile robot controllers and for implementing simulators running on webots platform.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahn, K.K., and D.C. Thanh. 2006. Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network. Mechatronics 16 (9): 577–587.
Arsene, O., C. Buiu, and N. Popescu. 2011. SNUPS-a simulator for numerical membrane computing. International Journal of Innovative Computing, Information and Control 7 (6): 3509–3522.
Bennett, S. 2001. The past of PID controllers. Annual Reviews in Control 25: 43–53.
Blazic, S. 2011. A novel trajectory-tracking control law for wheeled mobile robots. Robotics and Autonomous Systems 59 (11): 1001–1007.
Buiu, C. 2009. Towards integrated biologically inspired cognitive architectures. In Proceedings of the international conference on electronics, computers, and AI-ECAI’09, 2–8.
Buiu, C., O. Arsene, C. Cipu, and M. Patrascu. 2011. A software tool for modeling and simulation of numerical P systems. Biosystems 103: 442–447.
Buiu, C., A.B. Pavel, C.I. Vasile, and I. Dumitrache. 2011. Perspectives of using membrane computing in the control of mobile robots. In Proceedings of of the beyond AI-interdisciplinary aspect of artificial intelligence conference, 21–26.
Buiu, C., C.I. Vasile, and O. Arsene. 2012. Development of membrane controllers for mobile robots. Information Sciences 187: 33–51.
Campion, G., G. Bastin, and B. Dandrea-Novel. 1996. Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. IEEE Transactions on Robotics and Automation 12 (1): 47–62.
Chen, C., T. Li, and Y. Yeh. 2009. EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots. Information Sciences 179 (1–2): 180–195.
Chen, C., T. Li, Y. Yeh, and C. Chang. 2009. Design and implementation of an adaptive sliding-mode dynamic controller for wheeled mobile robots. Mechatronics 19 (2): 156–166.
Colomer, M.A., A. Margalida, D. Sanuy, and M.J. Pérez-Jiménez. 2011. A bio-inspired computing model as a new tool for modeling ecosystems: The avian scavengers as a case study. Ecological Modelling 222 (1): 33–47.
Cyberbotics Ltd., O. Michel, F. Rohrer, and N. Heiniger. 2010. Wikibooks contributors. Cyberbotics’ Robot Curriculum.
Cyberbotics, professional mobile robot simulation. http://www.cyberbotics.com.
DARPA, DARPA’s call for biologically inspired cognitive architectures research program. http://www.darpa.mil/ipto/programs/bica/bica.asp.
Epucksite, e-puck website. http://www.e-puck.org.
Epucksite, e-puck website. http://www.e-puck.org/images/electronics/shematics.png.
Garcia-Quismondo, M., A.B. Pavel, and M.J. Pérez-Jiménez. 2012. Simulating large-scale ENPS models by means of GPU. In Proceedings of the tenth brainstorming week on membrane computing, 137–152.
Hoffmann, H., M. Maggio, M.D. Santambrogio, A. Leva, and A. Agarwal. 2011.SEEC: A general and extensible framework for self-aware computing. Technical report MIT-CSAIL-TR-2011-046.
Hu, H., and P.Y. Woo. 2006. Fuzzy supervisory sliding-mode and neural network control for robotic manipulators. IEEE Transactions on Industrial Electronics 53 (3): 929–940.
Kanayama, Y., Y. Kimura, F. Miyazaki, and T. Noguchi. 1990. A stable tracking control method for an autonomous mobile robot. In Proceedings of the IEEE conference robotics and automation, 384–389.
Kukao, T., H. Nakagawa, and N. Adachi. 2000. Adaptive tracking control of nonholonomic mobile robot. IEEE Transactions on Robotics and Automation 16 (6): 609–615.
Lambercy, F., and G. Caprari. Khepera III manual ver 2.2, http://ftp.k-team.com/KheperaIII/Kh3.Robot.UserManual.2.2.pdf.
Moubarak, P., and P. Ben-Tzvi. 2011. Adaptive manipulation of a hybrid mechanism mobile robot. In Proceedings of IEEE international symposium on robotic and sensors environments (ROSE), 113–118.
Păun, Gh. 1999. Computing with membranes: An introduction. Bulletin of the EATCS 67: 139–152.
Păun, Gh, and R. Păun. 2006. Membrane computing and economics: Numerical P systems. Fundamenta Informaticae 73 (1): 213–227.
Păun, Gh, G. Rozenberg, and A. Salomaa (eds.). 2010. The Oxford Handbook of Membrane Computing. Oxford: Oxford University Press.
Pavel, A.B., O. Arsene, and C. Buiu. 2010. Enzymatic numerical P systems - a new class of membrane computing systems. In Proceedings of IEEE fifth international conference on bio-inspired computing: Theories and applications (BIC-TA), 1331–1336.
Pavel, A.B., and C. Buiu. 2012. Using enzymatic numerical P systems for modeling mobile robot controllers. Natural Computing 11 (3): 387–393.
Pavel, A.B., C.I. Vasile, and I. Dumitrache. 2012. Robot localization implemented with enzymatic numerical P systems. In Proceedings of the international conference on biomimetic and biohybrid systems, 204–215.
Pavel, A.B., C.I. Vasile, and I. Dumitrache. 2013. Membrane computing in robotics. In Beyond artificial intelligence, Series: Topics in Intelligent Engineering and Informatics, eds. J. Kelemen, J. Romportl, E. Zackova, Vol. 4, 125–136. Berlin: Springer.
Vasile, C.I., A.B. Pavel, I. Dumitrache, and Gh Păun. 2012. On the power of enzymatic numerical P systems. Acta Informatica 49 (6): 95–412.
Wang, X., G. Zhang, F. Neri, J. Zhao, M. Gheorghe, F. Ipate, and R. Lefticaru. 2016. Design and implementation of membrane controllers for trajectory tracking of nonholonomic wheeled mobile robots. Integrated Computer-Aided Engineering 23: 15–30.
Wang, T., G. Zhang, J. Zhao, Z. He, J. Wang, and M.J. Pérez-Jiménez. 2015. Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Transactions on Power Systems 30 (3): 1182–1194.
Wang, X., G. Zhang, J. Zhao, H. Rong, F. Ipate, and R. Lefticaru. 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): 725–738.
Website of the simulator for numerical P Systems (SNUPS). http://snups.buiu.net/, Laboratory of Natural Computing and Robotics, Politehnica University of Bucharest.
Webots robot simulator. http://www.cyberbotics.com/, Cyberbotics Ltd.
Wit, C.C.D., H. Olsson, K.J. Astrom, and P. Lischinsk. 1995. A new model for control of systems with friction. IEEE Transactions on Automatic Control 40 (3): 419–425.
Wu, W., H. Chen, and Y. Wang. 2001. Global trajectory tracking control of mobile robots. ACTA Automatic Sinica 27 (3): 325–331.
Xiao, J., Y. Huang, Z. Cheng, J. He, and Y. Niu. 2014. A hybrid membrane evolutionary algorithm for solving constrained optimization problems. Optik 125 (2): 897–902.
Xu, D., D.B. Zhao, and J.Q. Yi. 2009. Trajectory tracking control of omnidirectional wheeled mobile manipulators: Robust neural network-based sliding mode approach. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 39 (3): 788–799.
Ye, J. 2008. Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot. Neurocomputing 71 (7–9): 1561–1565.
Zhang, G., M. Gheorghe, and Y. Li. 2012. A membrane algorithm with quantum-inspired subalgorithms and its application to image processing. Natural Computing 11 (3): 701–717.
Zhang, G., F. Zhou, X. Huang, J. Cheng, M. Gheorghe, F. Ipate, and R. Lefticaru. 2012. A novel membrane algorithm based on particle swarm optimization for solving broadcasting problems. Journal of Universal Computer Science 18 (13): 1821–1841.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Zhang, G., Pérez-Jiménez, M.J., Gheorghe, M. (2017). Robot Control with Membrane Systems. In: Real-life Applications with Membrane Computing. Emergence, Complexity and Computation, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-55989-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-55989-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55987-2
Online ISBN: 978-3-319-55989-6
eBook Packages: EngineeringEngineering (R0)