DOI QR코드

DOI QR Code

퍼지추론기반 센서융합 이동로봇의 장애물 회피 주행기법

Fuzzy Inference Based Collision Free Navigation of a Mobile Robot using Sensor Fusion

  • 진태석 (동서대학교 메카트로닉스공학과)
  • 투고 : 2018.01.15
  • 심사 : 2018.02.22
  • 발행 : 2018.03.31

초록

This paper presents a collision free mobile robot navigation based on the fuzzy inference fusion model in unkonown environments using multi-ultrasonic sensor. Six ultrasonic sensors are used for the collision avoidance approach where CCD camera sensors is used for the trajectory following approach. The fuzzy system is composed of three inputs which are the six distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot's wheels, and three cost functions for the robot's movement, direction, obstacle avoidance, and rotation. For the evaluation of the proposed algorithm, we performed real experiments with mobile robot with ultrasonic sensors. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

키워드

참고문헌

  1. Y. Arai, T. Fujii, H. Asama and Y. Kataoka, "Adaptive behavior acquisition of collision avoidance among multiple autonomous mobile robots," Proc. IROS, pp. 1762-1767, (1997).
  2. J. Borenstein and Y. Koren, "Potential field methods and their inherent for mobile robot navigation," In Proc. IEEE Int Conf. Robotics and Automation, vol. 2, pp. 1398-1404, (1991).
  3. T. Hessburg and M. Tomizuka, "Fuzzy logic control for lane change maneuvers in lateral vehicle Guidance," IEEE Control Systems, vol. 14, no. 4, pp. 55-63, (1994). https://doi.org/10.1109/37.295971
  4. J. Miura and Y. Shirai, "Vision and Motion Planning for a Mobile Robot under Uncertainty," The International Journal of Robotics Research, Vol. 16, No. 6, pp. 806-825, (1997). https://doi.org/10.1177/027836499701600606
  5. H. Liu, N. Stoll, S. Junginger, and K. Thurow, "Mobile robot for life science automation," Int. J. Adv. Robot. Syst., vol. 10, pp.1-14, (2013). https://doi.org/10.5772/52938
  6. J. Miura and Y. Shirai, "Vision and Motion Planning for a Mobile Robot under Uncertainty," The International Journal of Robotics Research, Vol. 16, No. 6, pp. 806-825, (1997). https://doi.org/10.1177/027836499701600606
  7. A. Al-Mayyahi, W. Wang, P. Birch, "Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation," Robotics, Vol. 3, pp. 349-370, (2014). https://doi.org/10.3390/robotics3040349
  8. C. Chen, P. Richardson, "Mobile robot obstacle avoidance using short memory: A dynamic recurrent neuro-fuzzy approach," Trans. Inst. Measur. Control, Vol. 34, pp.148-164, (2012). https://doi.org/10.1177/0142331210366642
  9. R. Wai, C. Liu, and W. Lin, "Design of switching path-planning control for obstacle avoidance of mobile robot," Journal of the Franklin Institute, Vol. 348, No.4, pp.718-737, (2011). https://doi.org/10.1016/j.jfranklin.2011.01.013
  10. T.S. Jin, "Control and Calibration for Robot Navigation based on Light's Panel Landmark," Journal of the Korean Society of Industry Convergence, Vol. 20, No. 2, pp.89-95, (2017). https://doi.org/10.21289/KSIC.2017.20.2.089