single-rb.php

JRM Vol.25 No.1 pp. 201-210
doi: 10.20965/jrm.2013.p0201
(2013)

Paper:

Indoor Localization of Flying Robot by Means of Infrared Sensors

Daisuke Iwakura and Kenzo Nonami

Division of Artificial Systems Science, Department of Mechanical Engineering, Graduate School, Chiba University, 1-33 Yayoi-cyo, Inage-ku, Chiba 263-8522, Japan

Received:
July 4, 2012
Accepted:
October 9, 2012
Published:
February 20, 2013
Keywords:
localization, particle filter, flying robot, unmanned aerial vehicle, quad-rotor
Abstract

Autonomous navigation of flying robots in GPSdenied environments such as indoors requires that the flying robot be able to recognize the environment using external sensors. Laser scanners and computer vision are mainly used for indoor mapping and localization in studies on indoor flight. However, such systems require higher payload capacity and processing power for the sensors. In this study, we develop a lightweight flying robot for achieving indoor autonomous flight using four infrared (IR) sensors. As the first stage of this study, we present a localization technique that involves the use of a particle filter. Two problems exist in our system. First, it is difficult to use IR sensors close to a wall, because doing so would yield faulty results when calculating distance using the sensor output voltage. To resolve this problem, we developed a probabilistic output voltage observation model. The particle filter estimates position from voltage information using this model without the use of calculated distance. The second problem is that the spatial resolution is low because only four IR sensors are used. This problem was solved by rotating the robot horizontally at all times to acquire information from various directions. The localization performance was verified experimentally using an electric turntable and a cart. In the first and second experiments, we confirmed that localization is successful even when the robot is in motion and even when the robot is flying near a wall.

Cite this article as:
Daisuke Iwakura and Kenzo Nonami, “Indoor Localization of Flying Robot by Means of Infrared Sensors,” J. Robot. Mechatron., Vol.25, No.1, pp. 201-210, 2013.
Data files:
References
  1. [1] D. H. Shim, H. J. Kim, and S. Sastry, “A Flight Control System for Aerial Robots: Algorithms and Experiments,” 15th IFAC World Congress on Automatic Control, 2002.
  2. [2] G. Hoffmann, D. G. Rajnarayan, S. L. Waslander, D. Dostal, J. S. Jang, and Claire J. Tomlin, “The Stanford Testbed of Autonomous Rotorcraft for Multi Agent Control (STARMAC),” In Proc. of the 23rd Digital Avionics Systems Conf. Salt Lake City, UT, 2004.
  3. [3] S. L. Waslander, G. M. Hoffmann, J. S. Jang, and C. J. Tomlin, “Multi-Agent Quadrotor Testbed Control Design: Integral Sliding Mode vs. Reinforcement Learning,” In Proc. of the Int. Conf. on Intelligent Robots and Systems (IROS’05), Edmonton, Canada, pp. 3712-3717, 2005.
  4. [4] H. Nakanishi, S. Kanata, and T. Sawaragi, “Improved Stability Using Environmental Adaptive Yaw Control for Autonomous Unmanned Helicopter and Bifurcation of Maneuvering in Turning,” J. of Robotics and Mechatronics, Vol.23, No.6, pp. 1091-1099, 2011.
  5. [5] H. Saiki, T. Fukao, T. Urakubo, and T. Kohno, “Hovering Control of Outdoor Blimp Robots Based on Path Following,” J. of Robotics and Mechatronics, Vol.23, No.2, pp. 207-214, 2011.
  6. [6] O. Amidi, T. Kanade, and R. Miller, “Vision-Based Autonomous Helicopter Research at Carnegie Mellon Robotics Institute 1991-1997,” In Proc. of the American Helicopter Society, Gifu, Japan, 1998.
  7. [7] K. Nonami, F. Kendoul, S. Suzuki, W. Wang, and D. Nakazawa, “Autonomous Flying Robots,” Springer, 2010.
  8. [8] S. Azrad, F. Kendoul, and K. Nonami, “Visual Survoing of Quadrotor Micro-Air Vehicle Using Color-Based Tracking Algorithm,” J. of System Design and Dynamics, Vol.4, No.2, 2010.
  9. [9] S. Grzonka, G. Grisetti, and W. Burgard, “A Fully Autonomous Indoor Quadrotor,” IEEE Trans. on robotics, Vol.28, No.1, pp. 90-100, 2012.
  10. [10] L. Meier, P. Tanskanen, L. Heng, G. H. Lee, F. Fraundorfer, and M. Pollefeys, “PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision,” Autonomous Robots, Springer, 2012.
  11. [11] M. Achtelik, A. Bachrach, R. He, S. Prentice, and N. Roy, “Autonomous navigation and exploration of a quadrotor helicopter in GPS-denied indoor environments,” In Robotics: Science and Systems Conf., 2008.
  12. [12] J. F. Roberts, T. S. Stirling, J. C. Zufferey, and D. Floreano, “Quadrotor Using Minimal Sensing For Autonomous Indoor Flight,” European Micro Air Vehicle Conf. and Flight Competition (EMAV), Toulouse, France, 2007.
  13. [13] D. M. Sobers, Jr., G. Chowdhary, and E. N. Johnson, “Indoor Navigation for Unmanned Aerial Vehicles,” American Institute of Aeronautics and Astronautics Guidance, Navigation, and Control Conf., Chicago, pp. 1-29, 2009.
  14. [14] D. Pebrianti, W.Wang, D. Iwakura, Y. Song, and K. Nonami, “Sliding Mode Controller for Stereo Vision Based Autonomous Flight of Quad-Rotor MAV,” J. of Robotics and Mechatronics, Vol.23, No.1, pp. 137-148, 2011.
  15. [15] H. Oh, D.-Y.Won, S.-S.Huh, D. H. Shim,M.-J. Tahk, and A. Tsourdos, “Indoor UAV Control Using Multi-Camera Visual Feedback,” J. of Intelligent & Robotic Systems, Vol.61, No.1-4, pp. 57-84, 2011.
  16. [16] J. F. Roberts, T. Stirling, J. C. Zufferey, and D. Floreano, “3-D relative positioning sensor for indoor flying robots,” Autonomous Robots, Springer, Vol.33, No.1-2, pp. 5-20, 2012.
  17. [17] J. Eckert, R. German, and F. Dressler, “On Autonomous Indoor Flights: High-Quality Real-Time Localization using Low-Cost Sensors,” IEEEWorkshop on Wireless Sensor Actor and Actuator Networks (WiSAAN 2012) Ottawa Canada, 2012.
  18. [18] M. Hehn and R. D’Andrea, “A Flying Inverted Pendulum,” IEEE Int. Conf. on Robotics and Automation (ICRA 2011), Shanghai, China, pp. 763-770, 2011.
  19. [19] D. Mellinger, N. Michael, and V. Kumar, “Trajectory generation and control for precise aggressive maneuvers with quadrotors,” The Int. J. of Robotics Research, Version of Record – April 19, 2012.
  20. [20] M. F. Abas, D. Pebrianti, S. Azrad, D. Iwakura, Y. Song, K. Nonami, and D. Fujiwara, “Circular Leader-Follower Formation Control of Quad-rotor Aerial Vehicles,” J. of Robotics and Mechatronics, Vol.25, No.1, 2013.
  21. [21] A. Mokhtari, A. Benallegue, and A. Belaidi, “Polynomial Linear Quadratic Gaussian and Sliding Mode Observer for a Quadrotor Unmanned Aerial Vehicle,” J. of Robotics and Mechatronics, Vol.17, No.4, pp. 483-495, 2005.
  22. [22] T. Toksoz, J. Redding, M. Michini, B. Michini, and J. P. How, “Automated Battery Swap and Recharge to Enable Persistent UAV Missions,” AIAA infotech@Aerospace Conf. 2011, AIAA-2011-1405, St. Louis, Missouri, USA, 2011.
  23. [23] H. Ayusawa, D. Iwakura, D. Nguyen, X. Wu, K. Nonami, and D. Fujiwara, “Development of the Autonomous Battery Exchanging System for MAV,” The Int. Conf. on Intelligent Unmanned Systems (ICIUS 2011) MoPmA1-4, 2011.
  24. [24] B. Griffin and C. Detweiler, “Resonant Wireless Power Transfer to Ground Sensors from a UAV,” IEEE Int. Conf. on Robotics and Automation (ICRA 2012), Saint Paul, Minnesota, USA, pp. 2660-2665, 2012.
  25. [25] D. Iwakura, W.Wang, K. Nonami, and M. Haley, “Movable Range-Finding Sensor System and Precise Automated Landing of Quad-Rotor MAV,” J. of System Design and Dynamics, Vol.5, No.1, pp. 17-29, 2010.
  26. [26] D. Iwakura, K. Nonami, and D. Fujiwara, “Indoor Localization of Flying Robot Equipped with Infrared Distance Sensors (in Japanese),” Proc. of the 12th Symp. on Motion And Vibration Control, pp. 253-258, 2011.
  27. [27] G. Kitagawa, “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” J. of Computational and Graphical Statistics, Vol.5, pp. 1-25, 1996.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 08, 2021