Skip to main content
SpringerLink
Log in

Path planning of fire-escaping system for intelligent building

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

We present the path-planning techniques of the fire-escaping system for intelligent building, and use multiple mobile robots to present the experimental scenario. The fire-escaping system contains a supervised computer, an experimental platform, some fire-detection robots and some navigation robots. The mobile robot has the shape of a cylinder, and its diameter, height and weight are 10 cm, 15 cm and 1.5 kg, respectively. The mobile robot contains a controller module, two DC servomotors (including drivers), three IR sensor modules, a voice module and a wireless RF module. The controller of the mobile robot acquires the detection signals from reflective IR sensors through I/O pins and receives the command from the supervised computer via wireless RF interface. The fire-detection robot carries the flame sensor to detect fire sources moving on the grid-based experiment platform, and calculates the more safety escaping path using piecewise cubic Bezier curve on all probability escaping motion paths. Then the user interface uses A* searching algorithm to program escaping motion path to approach the Bezier curve on the grid-based platform. The navigation robot guides people moving to the safety area or exit door using the programmed escaping motion path. In the experimental results, the supervised computer programs the escaping paths using the proposed algorithms and presents movement scenario using the multiple smart mobile robots on the experimental platform. In the experimental scenario, the user interface transmits the motion command to the mobile robots moving on the grid-based platform, and locates the positions of fire sources by the fire-detection robots. The navigation robot guides people leaving the fire sources using the low-risk escaping motion path and moves to the exit door.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Wang XH, Xiao JN, Bao MZ (2000) A ship fire alarm system based on fuzzy neural network. In: Proceedings of the 3rd World Congress on intelligent control and automation, Hefei, 28 June–2 July, pp 1734–1736

  2. Healey G, Slater D, Lin T, Drda B, Goedeke AD (1993) A system for real-time fire detection. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, New York, 15–17 June, pp 605–606

  3. Neubauer A (1997) Genetic algorithms in automatic fire detection technology. In: Second international conference on genetic algorithms in engineering systems: innovations and applications, Glasgow, 2–4 September, pp 180–185

  4. Ruser H, Magori V (1998) Fire detection with a combined ultrasonic–microwave Doppler sensor. In: Proceedings of IEEE ultrasonics symposium, Sendai, 5–8 October, pp 489–492

  5. Luo RC, Su KL, Tsai KH (2002) Fire detection and isolation for intelligent building system using adaptive sensory fusion method. In: The IEEE international conference on robotics and automation, Washington, 11–15 May, pp 1777–1781

  6. Chia SH, Su KL, Shiau SV, Chein TL (2011) Multilevel multisensory-based security system for an intelligent home. Artif Life Robot 16(1):10–15

    Article  Google Scholar 

  7. Shiau SV, Su KL, Wang CC, Guo JH (2011) Path planning of a multiple mobile robot system. Artif Life Robot 16(1):5–9

    Article  Google Scholar 

  8. Khatib M, Jaouni H, Chatila R, Laumond JP (1997) Dynamic path modification for car-like nonholonomic mobile robot. IN: IEEE international conference on robot and automation, Albuquerque, 20–25 April, pp 2920–2925

  9. Hwang JH, Arkin RC, Kwon D (2003) Mobile robots at your fingertip: Bezier curve on-line trajectory generation for supervisory control. In: IEEE/RSJ international conference robots system, Las Vegas, 27–31 October, pp 1444–1449

  10. Komoriya K, Tanie K (1989) Trajectory design and control of a wheel-type mobile robot using B-spline curve. In: IEEE international workshop on intelligent robots and systems, Tsukuba, 4–6 September, pp 398–405

  11. Su KL, Chung CY, Tzou JT (2011) A* searching algorithm applying in Chinese chess game. Int J Artif Life Robot 16(2):132–136

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Science Council of Taiwan (NSC 100-2221-E-224-018).

Author information

Authors and Affiliations

  1. Graduate School Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Hsu-Shan Su

  2. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Kuo-Lan Su

Authors
  1. Hsu-Shan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuo-Lan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuo-Lan Su.

Additional information

This work was presented in part at the 17th International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 2012.

About this article

Cite this article

Su, HS., Su, KL. Path planning of fire-escaping system for intelligent building. Artif Life Robotics 17, 216–220 (2012). https://doi.org/10.1007/s10015-012-0045-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-012-0045-1

Keywords

Search

Navigation