ICA  Vol.3 No.2 , May 2012
Enhanced Map-Based Indoor Navigation System of a Humanoid Robot Using Ultrasound Measurements
ABSTRACT
During recent years, walking humanoid robots have gained popularity from wheeled vehicle robots in various assistive roles in human’s environment. Self-localization is a necessary requirement for the humanoid robots used in most of the assistive tasks. This is because the robots have to be able to locate themselves in their environment in order to accomplish their tasks. In addition, autonomous navigation of walking robots to the pre-defined destination is equally important mission, and therefore it is required that the robot knows its initiate location precisely. The indoor navigation is based on the map of the environment used by the robot. Assuming that the walking robot is capable of locating itself based on its initiate location and the distance walked from it, there are still factors that impair the map-based navigation. One of them is the robot’s limited ability to keep its direction when it is walking, which means that the robot is not able to walk directly from one point to another due to a stochastic error in walking direction. In this paper we present an algorithm for straightening the walking path using distance measurements by built-in sonar sensors of a NAO humanoid robot. The proposed algorithm enables the robot to walk directly from one point to another, which enables precise map-based indoor navigation.

Cite this paper
I. Bäck, J. Kallio and K. Mäkelä, "Enhanced Map-Based Indoor Navigation System of a Humanoid Robot Using Ultrasound Measurements," Intelligent Control and Automation, Vol. 3 No. 2, 2012, pp. 111-116. doi: 10.4236/ica.2012.32013.
References
[1]   H. Hirukawa, F. Kanehiro and K. Kaneko, “Humanoid Robotics Platforms Developed in HRP,” Journal of Robotics and Automation Systems, Vol. 48, No. 4, 2004, pp. 165-175. doi:10.1016/j.robot.2004.07.007

[2]   H. Yussof, M. Yamano, Y. Nasu and M. Ohka, “Humanoid Robot Navigation Based on Groping Locomotion Algorithm to Avoid an Obstacle,” A. Lazinica, Ed., Mobile Robots: Towards New Applications, I-Tech Education and Publishing, Vienna, 2006.

[3]   D. Feil-Seifer and M. J. Matanic, “Socially Assistive Robotics,” IEEE Robotics and Automation Magazine, Vol. 18, No. 1, 2011, pp. 24-31. doi:10.1109/MRA.2010.940150

[4]   A. Hornung, K. M. Wurm and M. Bennewitz, “Humanoid Robot Localization in Complex Indoor Environments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, 18-22 October 2010, pp. 1690-1695.

[5]   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, 2002.

[6]   A. Barrera, “Advances in Robot Navigation,” InTech, Vienna, 2011.

[7]   J. F. Seara and G. Schmidt, “Intelligent Gaze Control for Vision-Guided Humanoid Walking: Methodological Aspects,” Journal of Robotics and Autonomous System, Vol. 48, No. 4, 2004, pp. 231-248. doi:10.1016/j.robot.2004.07.003

[8]   K. Y. Tu and J. Baltes, “Fuzzy Potential Energy for a Map Approach to Robot Navigation,” Journal of Robotics and Autonomous Systems, Vol. 54, No. 7, 2006, pp. 574589. doi:10.1016/j.robot.2006.04.001

[9]   A. Clerentin, L. Delahoche, E. Brassart and C. Drocourt, “Self Localization: A New Uncertainty Propagation Architecture,” Journal of Robotics and Autonomous Systems, Vol. 51, No. 2-3, 2005, pp. 151-166. doi:10.1016/j.robot.2004.11.002

[10]   Aldebaran Robotics. http://www.aldebaran-robotics.com/

[11]   E. W. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” Numerische Mathematik, Vol. 1, No. 1, 1959, pp. 269-271. doi:10.1007/BF01386390

 
 
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