Service Robot Using Estimation of Body Direction Based on Gait for Human-Robot Interaction

  • Ayanori YorozuEmail author
  • Masaki Takahashi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)


Recently, there have been several studies on the research and development of service robots, and experimental results in real environments have been reported. To realize socially acceptable human-robot interaction for service robots, human recognition, including not only position but also body direction, around the robot is important. Using an RGB-D camera, it is possible to detect the posture of a person. However, because the viewing angle of the camera is narrow, it is difficult to recognize the environment around the robot with a single device. This study proposes the estimation of the body direction based on the gait, that is, not only the position and velocity, but also the state of the legs (stance or swing phase), using laser range sensors installed at shin height. We verify the effectiveness of the proposed method for several patterns of movement, which are seen when a person interacts with the service robot.


Service robots Human-robot interaction Gait measurement Kalman filter 



This study was supported by JSPS KAKENHI Grant Number 17K14619 and “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3.


  1. 1.
  2. 2.
    Nakamura, K., Morita, T., Yamaguchi, T.: PRINTEPS for development integrated intelligent applications and application to robot teahouse. In: International Conference on Web Intelligence, pp. 1199–1206 (2017)Google Scholar
  3. 3.
    Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N.: A robot that approaches pedestrians. IEEE Trans. Robot. 29(2), 508–524 (2013)CrossRefGoogle Scholar
  4. 4.
    Hu, J.S., Wang, J.J., Ho, D.M.: Design of sensing system and anticipative behavior for human following of mobile robots. IEEE Trans. Ind. Electron 61(4), 1916–1927 (2014)CrossRefGoogle Scholar
  5. 5.
    Karunarathne, D., Morales, Y., Kanda, T., Ishiguro, H.: Model of side-by-side walking without the robot knowing the goal. Int. J. Soc. Robot. 10, 1–20 (2017)Google Scholar
  6. 6.
    Carballo, A., Ohya, A., Yuta, S.: Reliable people detection using range and intensity data from multiple layers of laser range finders on a mobile robot. Int. J. Soc. Robot. 3(2), 167–186 (2011)CrossRefGoogle Scholar
  7. 7.
    Yorozu, A., Moriguchi, T., Takahashi, M.: Improved leg tracking considering gait phase and spline-based interpolation during turning motion in walk tests. Sensors 15(9), 22451–22472 (2015)CrossRefGoogle Scholar
  8. 8.
    Yorozu, A., Takahashi, M.: Navigation for gait measurement robot evaluating dual-task performance considering following human in living space. In: Workshop on Assistance and Service Robotics in a Human Environment in Conjunction with IEEE/RSJ International Conference on Intelligent Robots and Systems (2016)Google Scholar
  9. 9.
    Matsumura, T., Moriguchi, T., Yamada, M., Uemura, K., Nishiguchi, S., Aoyama, T., Takahashi, M.: Development of measurement system for task oriented step tracking using laser range finder. J. NeuroEngineering Rehabil. 10, 47 (2013)CrossRefGoogle Scholar
  10. 10.
    Hokuyo Automatic Co., Ltd.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Science and TechnologyKeio UniversityYokohamaJapan
  2. 2.Department of System Design EngineeringKeio UniversityYokohamaJapan

Personalised recommendations