DOI QR코드

DOI QR Code

최적화 기법을 사용한 실내 이동 로봇의 위치 인식

An Optimization Approach for Localization of an Indoor Mobile Robot

  • 한준희 (조선대학교 대학원 제어계측공학과) ;
  • 고낙용 (조선대학교 전자공학과)
  • 투고 : 2016.04.01
  • 심사 : 2016.08.10
  • 발행 : 2016.08.25

초록

본 논문은 실내 주행 로봇의 위치 추정을 위해 최적화 기법을 적용한 방법에 대해 기술한다. 주행 로봇의 위치 추정에 사용되는 베이지안 필터 방법의 경우는 측정값과 환경 요소에 대한 불확실성을 고려하기위해 사용하는 조절 파라미터에 따라 추정성능이 달라진다. 또한 로봇동작 및 센서 측정 모델의 비선형성에 의하여 성능이 저하될 수 있다. 최적화 기법은 조절 파라미터가 적고 모델의 비선형성의 영향을 적게 받는다. 본 연구에서는 최적화 기법의 위치 추정 활용성을 보이기 위해 최적화 방법에 의한 추정성능과 EKF방법에 의한 추정 성능을 비교한다. 사용한 측정 센서는 초음파 위성 시스템(USAT, Ultrasonic Satellites system)으로서 4개의 비컨으로부터 로봇까지의 거리를 측정한다. 측정값의 비정상 오차를 제거하기 위하여 마할라노비스 거리(Mahalanobis Distance)를 이용한다. 최적화 기법은 거리 측정값을 사용하여 목적함수를 설계하고 반복계산을 통해 위치의 최적 값을 찾는다. 반복 수행을 위한 초기 위치를 베이시안 필터 방법을 통하여 적절히 설정함으로서 제안된 방법은 위치 추정 성능을 향상시키고 실행 시간을 단축시킬 수 있다.

This paper proposes a method that utilizes optimization approach for localization of an indoor mobile robot. Bayesian filters which have been widely used for localization of a mobile robot use many control parameters to take the uncertainties in measurement and environment into account. The estimation performance depends on the selection of these parameter values. Also, the performance of the Bayesian filters deteriorate as the non-linearity of the motion and measurement increases. On the other hand, the optimization approach uses fewer control parameters and is less influenced by the non-linearity than the Bayesian methods. This paper compares the localization performance of the proposed method with the performance of the extended Kalman filter to verify the feasibility of the proposed method. Measurements of ranges from beacons of ultrasonic satellite to the robot are used for localization. Mahalanobis distance is used for detection and rejection of outlier in the measurements. The optimization method sets performance index as a function of the measured range values, and finds the optimized estimation of the location through iteration. The method can improve the localization performance and reduce the computation time in corporation with Bayesian filter which provides proper initial location for the iteration.

키워드

참고문헌

  1. B. S. Choi, J. W. Lee, J. J. Lee, K. T. Park, "A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion", Industrial Electronics, vol. 58, no. 6, pp. 2226-2235, 2011. https://doi.org/10.1109/TIE.2011.2109330
  2. Y. Zhang, D. W. Gong, J. H. Zhang, "Robot path planning in uncertain environment using multi-objective particle swarm optimization", Nerocomputig, vol. 103, pp. 172-185, 2013. https://doi.org/10.1016/j.neucom.2012.09.019
  3. W. P. Yu, S. L. Choi, J. Y. Lee, S. H. Park, "Robot Navigation Technology an Its Standardization Trends", 전자통신동향분석, vol. 26, no. 6, 2011.
  4. Y. H. Ji, J. H. Bae, J. B. Song, J. K. Ryu, J. H. Baek, "Outdoor Localization through GPS Data and Matching of Lane Markers for a Mobile Robot", Journal of Institute of Control, Robotics and Systems, vol. 18, no. 6, pp. 594-600, 2012. https://doi.org/10.5302/J.ICROS.2012.18.6.594
  5. E. S. Park, C. H. Yu, J. W. Choi, "Development of a Lateral Control System for Autonomous Vehicles Using Data Fusion of Vision and IMU Sensors with Field Tests", Journal of Institute of Control, Robotics and Systems, vol. 21, no. 3, pp. 179-186, 2015. https://doi.org/10.5302/J.ICROS.2015.14.9009
  6. V. Nguyen, A. Martinelli, N. Tomatis, R. Siegwart, "A Comparison of Line Extraction Algorithms using 2D Laser Rangefinder for Indoor Mobile Robotics", Intelligent Robots and System, pp. 1929-1934, 2005.
  7. J. Y. Choi, S. G. Kim, "Study on the Localization Improvement of the Dead Reckoning using the INS Calibrated by the Fusion Sensor Network Information", Journal of Institute of Control, Robotics and Systems, vol. 18, no. 8, pp. 744-749, 2012. https://doi.org/10.5302/J.ICROS.2012.18.8.744
  8. J. M. Kim, Y. T Kim, S. S. Kim, "An Accurate Localization for Mobile Robot Using Extended Kalman Filter and Sensor Fusion", IEEE International Joint Conference on Neural Networks, pp. 2928-2933, 2008.
  9. Gabriel Hartmann, Fay Huang, Reinhard Klette, "Landmark Initialization for Unscented Kalman Filter Sensor Fusion in Monocular Camera Localization", International Journal of Fuzzy Logic and Intelligent Systems, vol. 13, no. 1, pp. 1-10, 2013. https://doi.org/10.5391/IJFIS.2013.13.1.1
  10. T. G. Kim, N. Y. Ko, S. W. Noh, "Simultaneous Estimation of Landmark Location and Robot Pose Using Particle Filter Method", Journal of Korean Institute of Intelligent Systems, vol. 22, no. 3, pp. 353-360, 2012. https://doi.org/10.5391/JKIIS.2012.22.3.353
  11. Hyunhak Cho, Jungmin Kim, Joocheol Do, Sungshin Kim, "Improvement of Positioning Accuracy of Laser Navigation System using Particle Filter", Journal of Korean Institute of Intelligent Systems, vol. 21, no. 6, pp. 755-760, 2011. https://doi.org/10.5391/JKIIS.2011.21.6.755
  12. C. Li, Y. Shen, L. Liu, Q. Cao, "An Optimization Algorithm for Wireless Sensor Networks Localization Using Multipler Method", Computational Science and Optimization, vol. 2, pp. 337-341, 2010.
  13. Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, Y. Bengio, "Identifying and attacking the saddle point problem in high-dimensional non-convex optimization", Advances in Neural Information Processing Systems, 2014.
  14. M. M. A. Abdelaziz, H.E. Farag, E. F. El-Saadany, Y. A. R. I. Mohamed, "A Novel and Generalized Three-Phase Power Flow Algorithm for Islanded Microgrids Using a Newton Trust Region Method", IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, no. 1, pp. 190-201, 2013. https://doi.org/10.1109/TPWRS.2012.2195785
  15. N. Y. Ko, T. G. Kim, D. J. Seo, Y. T. Seo, "Localization of Mobile Based Robot Using Ultrasonic Satellite", Proceedings of KIIS Spring Conference, vol. 19, no. 1, pp. 199-202, 2009.
  16. S. W. Noh, T. G. Kim, N. Y. Ko, "Map Building Using ICP Algorithm based a Robot Position Prediction", Korea Institute of Electronic Communication Science, vol. 8, no. 4, pp. 575-582, 2013. https://doi.org/10.13067/JKIECS.2013.8.4.575

피인용 문헌

  1. A Study on the Development of Taxi Safety Support System Using the Beacon Device vol.26, pp.6, 2016, https://doi.org/10.5391/JKIIS.2016.26.6.452