캡스톤 디자인을 통한 3D Depth 센서 기반 HRI 시스템의 위치추정 알고리즘 연구

A Study of Localization Algorithm of HRI System based on 3D Depth Sensor through Capstone Design

  • 투고 : 2016.10.28
  • 심사 : 2016.11.15
  • 발행 : 2016.11.30

초록

The Human Robot Interface (HRI) based on 3D depth sensor on the docent robot is developed and the localization algorithm based on extended Kalman Filter (EKFLA) are proposed through the capstone design by graduate students in this paper. In addition to this, the performance of the proposed EKFLA is also analyzed. The developed HRI system consists of the route generation and localization algorithm, the user behavior pattern awareness algorithm, the map data generation and building algorithm, the obstacle detection and avoidance algorithm on the robot control modules that control the entire behaviors of the robot. It is confirmed that the improvement ratio of the localization error in EKFLA on the scenarios 1-3 is increased compared with the localization algorithm based on Kalman Filter (KFLA) as 21.96%, 25.81% and 15.03%, respectively.

키워드

참고문헌

  1. A. Steinfeld, T. Fong, D. Kaber, M. Lewis, J. Scholtz, A. Schultz, and M. Goodrich(2006), Common Metrics for Human-robot Interaction, In Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction : 33-40.
  2. C.Y Choi, E. Myagrmar, D.M Lee, S.R Kwon and H. Choi(2013) Design of HRI System based on 3D Depth Sensor for Awareness of Robot Obstacles and Behaviors, in Proc. KICS Int. Conf. Fall. 2013 : 192-198.
  3. A Development of the HRI System based on 3D Depth Sensor for Awareness of Robot Obstacles and Behaviors(2014), Final Report of 2013 IT/SW Creative Research Process (Technologies Development), National IT Industry Promotion Agency(NIPA) : 15-16.
  4. ROS (Robot Operating System), http://wiki.ros.org
  5. D. Simon(2001), Kalman Filtering, Embedded Systems Programming : 72-79.
  6. E.F. Schneider and D. Wildermuth(2004), Using Extended Kalman filter for Relative Localization in a Moving Robot Formation, 4th International Workshop on Robot Motion and Control : 85-90.
  7. G. Welch, and G. Bishop(2001), An Introduction to the Kalman Filter, ACM SIGGRAPH Tutorial.
  8. R. Negenborn(2003), Robot Localization and Kalman Filters: On Finding Your Position in a Noisy World, Utrecht Univ. Master's thesis.