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Topometric Navigation Considering Movable Objects

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Intelligent Autonomous Systems 16 (IAS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 412))

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Abstract

Recently, the demand for mobile manipulator-type human support robots has been increasing. Human support robots recognize the surrounding environment using a previously generated environmental map and perform navigation, such as localization and path planning. Conventional environment maps do not consider characteristics of objects and do not have any option to move an object to clear a path making it difficult for robots to move efficiently when an object is placed on their path. In this study, we propose topological navigation that uses a topological map based on sensing information and representing a path that robots can pass, and a metric map based on geometrical information of objects and representing the presence of objects. Consequently, if necessary, it is possible for robots to move a movable object to clear the passage and realize efficient movement in living environments.

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References

  1. Kimura, N., Ota, J.: Unknown object detection using floor height map for mobile robots on indoor floor with non-horizontal partial areas. J. Robot. Soc. Jpn. 34(10), 699–710 (2017)

    Article  Google Scholar 

  2. Kostavelis, I., Gasteratos, A.: Semantic mapping for mobile robotics tasks: a survey. Robot. Auton. Syst. 66, 86–103 (2015)

    Article  Google Scholar 

  3. Yuichi, T.: Mobile robot navigation using topological map representations. J. Robot. Soc. Jpn. 33(10), 9–10 (2015)

    Google Scholar 

  4. Kimura, N.: Environment recognition for automated guided vehicles in factory and warehouse. J. Robot. Soc. Jpn. 35(9), 652–655 (2017)

    Article  Google Scholar 

  5. Iliakopoulou, A., et al.: A review of global path planning methods for occupancy grid maps regardless of obstacle density. J. Intell. Robot. Syst. 84(1–4), 829–858 (2016)

    Google Scholar 

  6. Kostavelis, I., Gasteratos, A.: Learning spatially semantic representations for cognitive robot navigation. Robot. Auton. Syst. 61(12), 1460–1475 (2013)

    Article  Google Scholar 

  7. Naik, L., Blumenthal, S., Huebel, N., Bruyninckx, H., Prassler, E.: Semantic mapping extension for OpenStreetMap applied to indoor robot navigation, pp. 3839–3845 (2019)

    Google Scholar 

  8. Mozos, Ó.M., Burgard, W.: Supervised learning of topological maps using semantic information extracted from range data. In: IEEE Int. Conf. Intell. Robot. Syst., pp. 2772–2777 (2006)

    Google Scholar 

  9. Magid, E., Lavrenov, R., Afanasyev, I.: Voronoi-based trajectory optimization for UGV path planning. In: 2017 Int. Conf. Mech. Syst. Control Eng. ICMSC 2017, pp. 383–387 (2017)

    Google Scholar 

  10. Seda, M., Pich, V.: Robot motion planning using generalised voronoi diagrams. In: ISCGAV 2008 Proc. 8th Wseas Int. Conf. Signal Process. Comput. Geom. Artif. Vis., pp. 215–220 (2008)

    Google Scholar 

  11. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proc. IEEE Int. Conf. Comput. Vis., pp. 2980–2988 (2017)

    Google Scholar 

  12. cocodataset.org. “Common Objects in Context.” https://cocodataset.org/#home (2021)

  13. Microsoft. “Azure Kinect DK.” https://azure.microsoft.com/ja-jp/services/kinect-dk/ (2019)

  14. Sugihara, K.: Nawabari no suurimoderu: voronoi-zu kara no surikougaku-nyumon. Kyoritsu Shuppan (2009)

    Google Scholar 

  15. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  Google Scholar 

  16. Hart, P.E., Nilsson, N.J., Raphael, B.: formal basis for the heuristic determination EIJJ. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Google Scholar 

  17. ROS. “home page.” http://wiki.ros.org/ (2020)

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Acknowledgments

This work was supported by Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) [grant number JPMJCR19A1].

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Authors and Affiliations

  1. Department of System Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan

    Shunsuke Mochizuki & Masaki Takahashi

  2. Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan

    Ayanori Yorozu

Authors
  1. Shunsuke Mochizuki
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  2. Ayanori Yorozu
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  3. Masaki Takahashi
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Corresponding author

Correspondence to Shunsuke Mochizuki .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Marcelo H. Ang Jr

  2. Department of Precision Engineering, University of Tokyo, Tokyo, Japan

    Hajime Asama

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  4. Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

    Shaohui Foong

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Mochizuki, S., Yorozu, A., Takahashi, M. (2022). Topometric Navigation Considering Movable Objects. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-95892-3_7

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