Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Intelligent System based on Command Fusion and Fuzzy Logic Approaches - Application to mobile robot navigation

명령융합과 퍼지기반의 지능형 시스템-이동로봇주행적용

  • Jin, Taeseok (Dept. of Mechatronics Engineering, Dongseo University) ;
  • Kim, Hyun-Deok (Department of Electronics Engineering, Gyeongnam National University of Science and Technology)
  • Received : 2014.03.28
  • Accepted : 2014.04.30
  • Published : 2014.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper propose a fuzzy inference model for obstacle avoidance for a mobile robot with an active camera, which is intelligently searching the goal location in unknown environments using command fusion, based on situational command using an vision sensor. Instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. In this paper, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. We describe experimental results obtained with the proposed method that demonstrate successful navigation using real vision data.

본 논문은 능동카메라가 장착된 이동로봇의 장애물 회피를 위한 퍼지추론방법 제시하였다. 영상센서를 이용하여 상황적 판단에 근거한 명령융합을 사용하여 미지의 환경에서의 목적지까지의 지능적인 탐색을 수행하도록 하였다. 본 연구를 검증하기 위하여 환경모델과 센서데이터에 기반 한 이동로봇의 경로생성을 위한 물리적 센서융합을 시도하지 않고, 환경에 따른 각각의 로봇의 주행행동을 제어하기 위한 명령융합 적용하였다. 주행을 위한 전략으로는 목적지 접근과 장애물 회피를 수행할 수 있도록 퍼지규칙 조합을 통해 판단하도록 수행하였다. 제안한 방법을 검증하기 위하여 영상데이터를 사용한 성공적인 주행 실험 결과를 제시하였다.

Keywords

References

  1. M.J. Er, T.P. Tan, and S.Y. Loh, "Control of a mobile robot using generalized dynamic fuzzy neural networks," Microprocessors and Microsystems, vol. 28, pp. 491-498, Nov. 2004. https://doi.org/10.1016/j.micpro.2004.04.002
  2. D. Nair, J.K. Aggarwal, "Moving Obstacle Detection from a Navigation Robot," IEEE Transaction on robotics and automation, vol. 14, no. 3, pp. 404-416, 1998. https://doi.org/10.1109/70.678450
  3. H.R. Beom, H.S. Cho, "A sensor-Based Navigation for a Mobile Robot Using Fuzzy Logic and Reinforcement Learning," IEEE Transaction on system, man, and cybernetics, vol. 25, no. 3, pp. 464-477, 1995. https://doi.org/10.1109/21.364859
  4. A. Ohya, A. Kosaka, and A. Kak, "Vision-Based Navigation by a Mobile Robot with Obstacle Avoidance Using Single-Camera Vision and Ultrasonic Sensing," IEEE Transactions on Robotics and Automation, vol. 14, no. 6, pp. 969-978, 1998. https://doi.org/10.1109/70.736780
  5. E. Tunstel, "Fuzzy-behavior synthesis, coordination, and evolution in an adaptive behavior hierarchy," in Fuzzy Logic Techniques for Autonomous 470 TUNSTEL, de OLIVEIRA, AND BERMAN Vehicle Navigation, Studies in Fuzziness and Soft Computing, chapter 9. Springer-Verlag, Heidelberg, 2000.
  6. L. Cherroun, and M. Boumehraz, "Intelligent systems based on reinforcement learning and fuzzy logic approaches-Application to mobile robotic," in Proceeding of 2012 International Conference on Information Technology and e-Services (ICITeS), pp. 1-6, 2012.
  7. G. Leng, T.M. McGinnity, and G. Prasad, "An approach for on-line extraction of fuzzy rules using a self-organizing fuzzy neural network," Fuzzy Sets and Systems, vol. 150, pp. 211-243, 2005. https://doi.org/10.1016/j.fss.2004.03.001
  8. T. Nishina, and M. Hagiwara, "Fuzzy inference neural network," Neurocomputing, vol.14, no.3, pp. 223-239, 1997. https://doi.org/10.1016/S0925-2312(96)00036-7
  9. T.S. Jin and J.M. Lee, "Trajectory Generation of a Moving Object for a Mobile Robot in Predictable Environment," International Journal of Precision Engineering and Manufacturing, vol. 5, no. 1, pp. 27-35, 2004.
  10. D.S. Wang, Y.S. Zhang, and W.J. Si, "Behavior-based hierarchical fuzzy control for mobile robot navigation in dynamic environment," in Proceeding of 2011 Chinese Control and Decision Conference(CCDC), pp. 2419-2424, 2011.