Toward a Robot That Acquires Logical Recognition of Space

  • Megumi Fujita Nara Women's University
  • Yuki Goto Kobe University
  • Naoyuki Nide Nara Women's University
  • Ken Satoh National Institute of Informatics
  • Hiroshi Hosobe Hosei University
Keywords: Autonomous robots, Situation recognition, Logical inference of actions


For cooperation between robots and humans, a robot should have logical recognition regarding space. For example, if a robot, such as a housekeeping robot, can recognize the arrangement structure of the furniture and the concept of “a room”, this information will be useful for asking the robot to carry out tasks. Therefore, our aim is for the robot to acquire information on the relationship between locations while moving. So, in hopes that the robot can recognize that “I have exited the room”, we conducted an initial-stage experiment involving a robot leaving a room using knowledge of the relationship between the entrance of the room and the door. This paper describes the insights obtained from this experiment.


M. Fujita, Y. Goto, N. Nide, K. Satoh, and H. Hosobe. Autonomous control of mobile robots using logical representation of map and inference of location. In Proc. of IEEE ICA2016, pages 78–81, 2016.

A. S. Rao and M. P. Georgeff. Modeling Rational Agents within a BDI-Architecture. In M. N. Huhns and M. P. Singh, editors, Readings in Agents, pages 317–328. Morgan Kaufmann, San Francisco, 1997.

M. E. Bratman. Intention, Plans, and Practical Reason. Harvard University Press, 1987.

R. H. Bordini, J. F. Hubner, and M. Wooldridge. ¨ Programming Multi-Agent Systems in AgentSpeak using Jason. John Wiley & Sons, 2007.

M. Fujita, H. Katayama, N. Nide, and S. Takata. BDI robots who adapt to the diversity of the real world. IPSJ Transactions on MPS, 5(1):50–64, 2012. (In Japanese).

E. Fernandez, L. S. Crespo, A. Mahtani, and A. Martinez. ´ Learning ROS for Robotics Programming — second edition. Packt Publishing, 2015.

M. Fujita, Y. Goto, N. Nide, K. Satoh, and H. Hosobe. Logic-based and robust decision making for robots in real world. In Proc. of AAMAS ’14, pages 1685–1686, 2014.

M. Fujita, Y. Goto, N. Nide, K. Satoh, and H. Hosobe. An architecture for autonomously controlling robot with embodiment in real world. In Proc. of Knowledge Representation and Reasoning in Robotics (workshop at ICLP 2013), pages 59–71, 2013.

K. Kimoto, N. Asada, T. Mori, Y. Hara, A. Ohya, and S. Yuta. Development of small size 3D LIDAR. In Proc. of IEEE ICRA2014, pages 4620–4626, 2014.

Hokuyo automatic co., ltd. Scanning rangefinder distance data output/YVT35LX product details. Viewed on Dec 09, 2017 (In Japanese).

S. Zhang, M. Sridharan, M. Gelfond, and J. Wyatt. An architecture for knowledge representation and reasoning in robotics. In Proc. of 15th International Workshop on Non-Monotonic Reasoning, pages 233–241, 2014.

A. Nuchter and J. Hertzberg. Towards semantic maps for mobile robots. ¨ Robotics and Autonomous Systems, 56(11):915–926, 2008.

A. Nuchter, K. Lingemann, J. Hertzberg, and H. Surmann. 6D SLAM — 3D mapping ¨ outdoor environments. Journal of Field Robotics, 24(8-9):699–722, 2007.

M. Ghallab, D. S. Nau, and P. Traverso. Automated Planning: Theory and Practice. Morgan Kaufmann Publishers Inc., 2004.

G. Lidoris, F. Rohrmuuller, D. Wollherr, and M. Buss. The autonomous city explorer ¨ (ACE) project — mobile robot navigation in highly populated urban environments. In Proc. of IEEE ICRA2009, pages 1416–1422, 2009.

C. Brandstatter, S. Schaat, A. Wendt, and M. Fittner. How agents use breadcrumbs to find their way. Journal of Computers, 12(1):89–96, 2017.

Technical Papers (Advanced Applied Informatics)