The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Localization Algorithms for Mobile Robots with Presence of Data Missing in a Wireless Communication Environment

무선통신 환경에서 데이터 손실 시 모바일 로봇의 측위 알고리즘

  • 김신 (조선대학교 전자공학부) ;
  • 신성 (조선대학교 전자공학부) ;
  • 유성현 (조선대학교 전자공학부)
  • Received : 2023.06.27
  • Accepted : 2023.08.17
  • Published : 2023.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Mobile robots are widely used in industries because mobile robots perform tasks in various environments. In order to carry out tasks, determining the precise location of the robot in real-time is important due to the need for path generation and obstacle detection. In particular, when mobile robots autonomously navigate in indoor environments and carry out assigned tasks within pre-determined areas, highly precise positioning performance is required. However, mobile robots frequently experience data missing in wireless communication environments. The robots need to rely on predictive techniques to autonomously determine the mobile robot positions and continue performing mobile robot tasks. In this paper, we propose an extended Kalman filter-based algorithm to enhance the accuracy of mobile robot localization and address the issue of data missing. Trilateration algorithm relies on measurements taken at that moment, resulting in inaccurate localization performance. In contrast, the proposed algorithm uses residual values of predicted measurements in data missing environments, making precise mobile robot position estimation. We conducted simulations in terms of data missing to verify the superior performance of the proposed algorithm.

모바일 로봇은 다양한 환경에서 임무를 수행하기 때문에 산업 분야에서 크게 활용되고 있다. 모바일 로봇이 작업을 수행하기 위해서는 경로를 생성하고 장애물을 탐지하기 때문에 실시간으로 로봇의 정확한 위치를 파악하는 것은 중요하다. 특히, 실내 환경에서 자율주행하는 모바일 로봇은 주어진 일을 정해진 영역에서 수행할 때, 보다 정밀한 측위 성능이 요구된다. 모바일 로봇은 무선통신 환경에서 송수신 데이터의 손실이 빈번히 발생하며, 데이터 손실 발생 시 예측 기술을 통해 로봇 스스로 자신의 위치를 파악하여 임무 수행을 이어 나가야 한다. 본 논문에서는 모바일 로봇의 위치 추정 정확도를 향상시키고, 데이터 손실 문제를 해결하고자 확장 칼만 필터 기반의 알고리즘을 제안한다. 삼변측량은 해당 순간에만 측정한 값을 사용하여 측위 성능이 부정확한 반면, 제안한 알고리즘은 데이터 손실 환경에서 예측 측정값의 잔차를 이용하기 때문에 모바일 로봇의 정밀한 위치 추정이 가능하다. 제안한 알고리즘의 우수한 성능 검증을 위하여 데이터 손실이 없는 환경과 데이터 손실 환경에서 모바일 로봇의 시뮬레이션을 수행하였다.

Keywords

Acknowledgement

이 논문은 2023년도 조선대학교 연구비의 지원을 받아 연구되었음.

References

  1. G. N. Desouza and A. C. Kak, "Vision for mobile robot navigation: a survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, Feb. 2002, pp. 237-267. https://doi.org/10.1109/34.982903
  2. Y. Kim, J. Kang, J. Yoon, Y. Lee and S. Baek, "implementation of Camera-Based Autonomous Driving Vehicle for Indoor Delivery using SLAM," J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 4, Aug. 2022, pp. 687-694.
  3. S. You, C. Ahn, Y. S. Shmaliy, and S. Zhao, "Minimum weighted Frobenius norm discrete-time FIR filter with embedded unbiasedness," IEEE Transactions on Circuits Systems II: Express Briefs, vol. 65, no. 9, Sept. 2018, pp. 1284-1288. https://doi.org/10.1109/TCSII.2018.2810812
  4. T. Kim and N. Ko, "Comparison and Analysis of Metods for Localization of a Mobile Robot," The Journal of Korean Institute of Information Technology, vol 11, no 1, Jan. 2013, pp. 79-89. https://doi.org/10.14801/kiitr.2013.11.1.79
  5. C. C. Tsai, "A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements," IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 5, Oct. 1998, pp. 1399-1404. https://doi.org/10.1109/19.746618
  6. L. Jetto, S. Longhi, and G. Venturini, "Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots," IEEE Transactions on Robotics and Automation, vol. 15, no. 2, Apr. 1999, pp. 219-229. https://doi.org/10.1109/70.760343
  7. F. Thomas and L. Ros, "Revisiting trilateration for robot localization," IEEE Transactions on Robotics, vol. 21, no. 1, Feb. 2005, pp. 93-101. https://doi.org/10.1109/TRO.2004.833793
  8. H. Lee and D. Lee, "A Study on Localization System using 3D Triangulation Algorithm based on Dynamic Allocation of Beacon Node," The Journal of Korea Information and Communications Society, vol. 36, no. 4, Apr. 2011, pp. 378-385. https://doi.org/10.7840/KICS.2011.36B.4.378
  9. F. Thomas and L. Ros, "Revisiting trilateration for robot localization," IEEE Transactions on Robotics, vol. 21, no. 1, Feb. 2005, pp. 93-101. https://doi.org/10.1109/TRO.2004.833793
  10. S. You, C. Ahn, S. Zhao, and Y. S. Shmaliy, "Frobenius Norm-Based Unbiased Finite Impulse Response Fusion Filtering for Wireless Sensor Networks," IEEE Transactions Industrial Electronics, vol. 69, no. 2, Feb. 2022, pp. 1867-1876. https://doi.org/10.1109/TIE.2021.3055172
  11. S. You, J. Pak, C. Ahn, P. Shi, and M. Lim, "Unbiased Finite-Memory Digital Phase-Locked Loop," IEEE Transactions on Circuits Systems II: Express Briefs, vol. 63, no. 8, Aug. 2016, pp. 798-802. https://doi.org/10.1109/TCSII.2016.2531138
  12. S. Y. Chen, "Kalman Filter for Robot Vision: A Survey," IEEE Transactions on Industrial Electronics, vol. 59, no. 11, Nov. 2012, pp. 4409-4420. https://doi.org/10.1109/TIE.2011.2162714
  13. J. N. Greenberg and X. Tan, "Dynamic Optical Localization of a Mobile Robot Using Kalman Filtering-Based Position Prediction," IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, Oct. 2020, pp. 2483-2492. https://doi.org/10.1109/TMECH.2020.2980434
  14. S. You, C. Ahn, Y. S. Shmaliy, and S. Zhao, "Fusion Kalman and Weighted UFIR State Estimator With Improved Accuracy," IEEE Transactions on Industrial Electronics, vol. 67, no. 12, Dec. 2020, pp. 10713-10722. https://doi.org/10.1109/TIE.2019.2958278
  15. S. You, D. Pae, and H. Choi, "A Digital Phase-locked Loop design based on Minimum Variance Finite Impulse Response Filter with Optimal Horizon Size," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 4, Aug. 2021, pp. 591-598.
  16. G. Song, N. Ko, and H. Choi, "Attitude Estimation of Unnamed Vehicles Using Unscented Kalman Filter," J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 1, Feb. 2019, pp. 265-274.
  17. F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. Nordlund, "Particle Filters for Positioning, Navigation, and Tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, Feb. 2022, pp. 425-437. https://doi.org/10.1109/78.978396
  18. A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
  19. V. Fox, J. Hightower, L. Lin, D. Schulz, and G. Borriello, "Bayesian filtering for location estimation," IEEE Pervasive Computing, vol. 2, no. 3, 2003, pp. 24-33. https://doi.org/10.1109/MPRV.2003.1228524
  20. H. Kang, J. Yun, and J. Lee, "Localization for Mobile Robot by Selective Anchors in Indoor GPS and EKF," The Journal of Korea Robotics Society, vol. 6, no. 1, Mar. 2011, pp. 58-68. https://doi.org/10.7746/jkros.2011.6.1.058