Abstract
This chapter describes a process of design and implementation of a reactive navigation system for a smart mobile robot. Equipped with a webcam and distance sensors, the autonomous robot will explore an arena to locate a number of sites in a limited time all while avoiding the arena boundary and any obstacles it might encounter. A fuzzy behavior-based control scheme with adaptive membership functions has been taken as a proposed reactive navigation system. The tests of the proposed method were performed in a real robot using a UDOO Quad board, which is a single-board computer equipped with two CPU, and experiments demonstrated that this embedded system was able to successfully complete the autonomous navigation task in a real arena.
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- 1.
UDOO website: http://www.udoo.org/
- 2.
OpenCV website: http://opencv.org
- 3.
Scikit-fuzzy website: http://pythonhosted.org/scikit-fuzzy/overview.html
- 4.
Arduino IDE for UDOO: http://udoo.org/download/files/arduino-1.5.4-for__UDOO.tar.gz
References
Arena, P., Di Giamberardino, P., Fortuna, L., La Gala, F., Monaco, S., Muscato, G., Rizzo, A., & Ronchini, R. (2004). Toward a mobile autonomous robotic system for Mars exploration. Planetary and Space Science, 52, 23–30.
Assis, L. S., Soares, A. S., Coelho, C. J., & Baalen, J. V. (2016). An evolutionary algorithm for autonomous robot navigation. International Conference on Computational Science, 80, 2261–2265.
Hashikawa, F., & Morioka, K. (2015). An assistance system for building intelligent spaces based on mapsharing among a mobile robot and distributed sensors. International Journal on Smart Sensing and Intelligent Systems, 8(1), 1–25.
Lin, H.-I., & Tzeng, H. Jr. (2014). Search strategy of a mobile robot for radiation sources in an unknown environment. In International Conference on Advanced and Intelligent Systems (pp. 56–60).
Long, Z., Liang, X., & Yang, L. (2010). Some approximation properties of adaptive fuzzy systems with variable universe of discourse. Information Sciences, 180(16), 2991–3005.
Mathers, N., Goktogen, A., Rankin, J., & Anderson, M. (2012). Robotic mission to Mars: Hands-on, minds-on, web-based learning. Acta Astronautica, 80, 124–131.
Murphy, R. (2000). Introduction to AI robotics. Cambridge: MIT Press.
Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In IEEE International Conference on Robotics and Automation (pp. 1398–1404).
Kunwar, F., & Benhabib, B. (2008). Advanced predictive guidance navigation for mobile robots: A novel strategy for rendezvous in dynamic settings. International Journal on Smart Sensing and Intelligent Systems, 1(4), 858–890.
Putney, J. S. (2006). Reactive navigation of an autonomous ground vehicle using dynamic expanding zones. Thesis, Faculty of the Virginia Polytechnic Institute and State University.
Siegwart, R., & Nourbakhsh, I. N. (2004). Introduction to autonomous mobile robots. Cambridge: MIT Press.
Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots (2nd ed.). Cambridge: MIT Press.
Tang, S. H., Ang, C. K., Nakhaeinia, D., Karasfi, B., & Motlagh, O. (2013). A reactive collision avoidance approach for mobile robot in dynamic environments. Journal of Automation and Control Engineering, 1(1), 16–20.
UDOO Starting Manual. http://udoo.org/download/files/Documents/UDOO_Starting_Manual_beta0.4_11_28_2013.pdf.
Wang, R., Liu, Y. J., Yu, F. S., Wang, J. Y., & Yang, J. L. (2017). Adaptive variable universe of discourse fuzzy control for a class of nonlinear systems with unknown dead zones. International Journal of Adaptive Control and Signal Processing, 31(12), 1934–1951.
Xu, J., Qian, H., Ying, W., & Zhang, J. (2015). A deployment algorithm for mobile wireless sensor networks based on the electrostatic filed theory. International Journal on Smart Sensing and Intelligent Systems, 8(1), 516–537.
Zhang, Q., Yue, S. G., Yin, Q. J., & Zha, Y. B. (2013). Dynamic obstacle-avoiding path planning for robots based on modified potential field method. Intelligent Computing Theories and Technology, 9, 332–342.
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Njah, M., El-Hamdi, R. (2019). Design and Implementation of a Reactive Navigation System for a Smart Robot Using Udoo Quad. In: Derbel, N., Ghommam, J., Zhu, Q. (eds) New Developments and Advances in Robot Control. Studies in Systems, Decision and Control, vol 175. Springer, Singapore. https://doi.org/10.1007/978-981-13-2212-9_13
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DOI: https://doi.org/10.1007/978-981-13-2212-9_13
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