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Fuzzy logic controller with fuzzylab python library and the robot operating system for autonomous mobile robot navigation

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Abstrakty
EN
The navigation system of a robot requires sensors to perceive its environment to get a representation. Based on this perception and the state of the robot, it needs to take an action to make a desired behavior in the environment. The actions are defined by a system that processes the obtained information. This system can be based on decision rules defined by an expert or obtained by a training or optimization process. Fuzzy logic controllers are based on fuzzy logic on which degrees of truth are used on sy‐ stem variables and has a rule‐base that stores the knowledge about the operation of the system. In this paper a fuzzy logic controller is made with the Python fuzzylab library which is based on the Octave Fuzzy Logic Toolkit, and with the Robot Operating System (ROS) for autonomous navigation of the TurtleBot3 robot on a simulated and a real environment using a LIDAR sensor to get the distance of the objects around the robot.
Twórcy
  • Tijuana Institute of Technology, Tijuana, Mexico
  • Tijuana Institute of Technology, Tijuana, Mexico
autor
  • Tijuana Institute of Technology, Tijuana, Mexico
Bibliografia
  • [1] E. Avelar. “fuzzylab”. https://github.com/ITTcs/fuzzylab, 2019, Accessed on: 2020‑05‑28.
  • [2] H. R. Berenji, “Fuzzy q‑learning: a new approach for fuzzy dynamic programming”, vol. 1, 1994, 486–491, 10.1109/FUZZY.1994.343737.
  • [3] H. Boubertakh, M. Tadjine, and P.‑Y. Glorennec, “A new mobile robot navigation method using fuzzy logic and a modified q‑learning algorithm”, Journal of Intelligent & Fuzzy Systems, vol. 21, no. 1 and 2, 2010, 113–119, 10.3233/IFS‑2010‑0440.
  • [4] L. Cherroun and M. Boumehraz, “Intelligent systems based on reinforcement learning and fuzzy logic approaches, ”application to mobile robotic””, 2012, 1–6, 10.1109/ICITeS.2012.6216661.
  • [5] L. Cherroun, M. Boumehraz, and A. Kouzou, “Mobile robot path planning based on optimized fuzzy logic controllers”, 2019, 255–283,10.1007/978‑981‑13‑2212‑9_12.
  • [6] Y. Duan and Xin‑Hexu, “Fuzzy reinforcement learning and its application in robot navigation”, vol. 2, 2005, 899–904,10.1109/ICMLC.2005.1527071.
  • [7] M. Faisal, R. Hedjar, M. A. Sulaiman, and K. Al‑Mutib, “Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment”, International Journal of Advanced Robotic Systems, vol. 10, no. 1, 2013, 37,10.5772/54427.
  • [8] P. Y. Glorennec and L. Jouffe, “Fuzzy q‑learning”.In: Proceedings of 6th International Fuzzy Systems Conference, vol. 2, 1997, 659–662 vol.2,10.1109/FUZZY.1997.622790.
  • [9] L. Markowsky and B. Segee, “The octave fuzzy logic toolkit”. In: 2011 IEEE International Workshop on Open‑source Software for Scientific Computation, 2011, 118–125, 10.1109/OSSC.2011.6184706.
  • [10] J. M. Mendel, Uncertain Rule‑Based Fuzzy Systems: Introduction and New Directions, Springer, 2017.
  • [11] S. M. Raguraman, D. Tamilselvi, and N. Shivakumar, “Mobile robot navigation using fuzzy logic controller”. In: 2009 International Conference on Control, Automation, Communication and Energy Conservation, 2009, 1–5.
  • [12] P. Reignier, “Fuzzy logic techniques for mobile robot obstacle avoidance”, Robotics and Autonomous Systems, vol. 12, no. 3, 1994, 143 – 153, 10.1016/0921‑8890(94)90021‑3.
  • [13] A. Saf�iotti, “The uses of fuzzy logic in autonomous robot navigation”, Soft Computing, vol. 1, no. 4, 1997, 180–197, 10.1007/s005000050020.
  • [14] R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza, Introduction to Autonomous Mobile Robots, The Mit Press, 2011.
  • [15] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, The Mit Press, 2018.
  • [16] C. J. C. H. Watkins and P. Dayan, “Q‑learning”, Machine Learning, vol. 8, no. 3, 1992, 279–292, 10.1007/BF00992698.
  • [17] L. A. Zadeh, “Fuzzy sets”, Information and Control, vol. 8, no. 3, 1965, 338–353, 10.1016/S0019‑9958(65)90241‑X.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
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