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Obstacle Recognition by 3D Feature Extraction for Mobile Robot Navigation in an Indoor Environment

복도환경에서의 이동로봇 주행을 위한 3차원 특징추출을 통한 장애물 인식

  • 진태석 (동서대학교 메카트로닉스공학과)
  • Received : 2010.05.19
  • Accepted : 2010.08.11
  • Published : 2010.09.30

Abstract

This paper deals with the method of using the three dimensional characteristic information to classify the front environment in travelling by using the images captured by a CCD camera equipped on a mobile robot. The images detected by the three dimensional characteristic information is divided into the part of obstacles, the part of corners, and th part of doorways in a corridor. In designing the travelling path of a mobile robot, these three situations are used as an important information in the obstacle avoidance and optimal path computing. So, this paper proposes the method of deciding the travelling direction of a mobile robot with using input images based upon the suggested algorithm by preprocessing, and verified the validity of the image information which are detected as obstacles by the analysis through neural network.

본 논문에서는 이동로봇에 장착된 CCD 카메라를 통해 입력되는 영상에서 3차원 물체가 가지는 특징정보를 분석 및 추출하여하여 주행전방의 환경을 구분하는데 적용하게 된다. 복도 내에서 주행하는 로봇에 탑재된 카메라로 입력된 영상은 3차원 특징정보에 의해 장애물과 복도의 코너, 문으로 검출되어진다. 바닥의 장애물 정보 인식을 통한 이동로봇의 주행경로를 구하는데 있어 이들 세 가지는 최적의 경로 생성과 장애물 회피를 위한 매우 중요한 정보로 사용될 수 있다. 따라서, 본 논문에서는 입력영상을 전처리 후에 제안된 알고리즘을 기반으로한 이동로봇의 주행방향결정과, 입력 영상에서 신경망을 통하여 장애물 인식 및 특징정보 검출을 통한 이동로봇의 주행을 위한 선행 실험결과를 제시하였다.

Keywords

References

  1. Don Murray and Anup Basu, "Motion Tracking with an Active Camera," IEEE Trans. of Pattern Analysis and Machine Intelligence, Vol. 16, No. 5, pp. 449-459, May 1994. https://doi.org/10.1109/34.291452
  2. Dinesh Nair, Jagdishkumar K. Aggarwal, "Moving Obstacle Detection Form a Navigating," IEEE Transactions on Robotics and Automation, Vol. 14, No 3, June 1998.
  3. Li, Fuzzy-logic-based Reactive Behavior of an Autonomous Mobile system in Unknown Environments, Eng. Application Artificial Intelligent, 7(50), pp.521-531, 1994. https://doi.org/10.1016/0952-1976(94)90031-0
  4. Marsland, S., Nehmzow, U., & Shapiro, J..On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems, 51(2-3), 191-206, 2005. https://doi.org/10.1016/j.robot.2004.10.006
  5. Lewis, M. A., & Tan, K. H. High precision formation control of mobile robots using virtual structures. Autonomous Robots, 4(4), 387-403, 1997. https://doi.org/10.1023/A:1008814708459
  6. X. Yang, and M. Meng, Neural network approaches to dynamic collision-free trajectory generation, IEEE Trans. on Systems, Man, and Cybernetics, Part B, 31(3) pp.302-318, 2001. https://doi.org/10.1109/3477.931512
  7. A. Vasilyev and A. Kapishnikov, "Approximation of conditional probability function using artificial neural networks," in Int. Conference on Modelling and Simulation of Business Systems, pp. 79-81, 2003.
  8. D. Nguyen and B. Widrow, "Improving the learning speed of two-layer neural networks by choosing initial values of the adaptive weights," in Int. Joint Conference on Neural Networks, pp. 21-261, 1990.
  9. K. Mehrotra, C. Mohan, and S. Ranka, Elements of Artificial Neural Networks. Cambridge, MA, USA: MIT Press, 1997.
  10. R. E. Fayek, R. Liscano and G. M. Karam, "A System Architecture for a Mobile Robot based on Activities and a Blackboard control unit," Proc. IEEE Int. Conf. on Robotics and Automation, pp. 267-274, 1993.
  11. P. Stone, R. S. Sutton, and G. Kuhlmann, "Reinforcement learning for RoboCup-soccer keepaway," Adaptive Behavior, vol. 13, no. 3, pp. 165-188, 2005. https://doi.org/10.1177/105971230501300301
  12. T. Nakashima and H. Ishibuchi, "Mimicking dribble trajectories by neural networks for RoboCup soccer simulation," in IEEE Multi-Conference on Systems and Control, pp. 658-663, 2007.