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Escape planning in realistic fire scenarios with Ant Colony Optimisation

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Abstract

An emergency requiring evacuation is a chaotic event, filled with uncertainties both for the people affected and rescuers. The evacuees are often left to themselves for navigation to the escape area. The chaotic situation increases when predefined escape routes are blocked by a hazard, and there is a need to re-think which escape route is safest. This paper addresses automatically finding the safest escape routes in emergency situations in large buildings or ships with imperfect knowledge of the hazards. The proposed solution, based on Ant Colony Optimisation, suggests a near optimal escape plan for every affected person — considering dynamic spread of fires, movability impairments caused by the hazards and faulty unreliable data. Special focus in this paper is on empirical tests for the proposed algorithms. This paper brings together the Ant Colony approach with a realistic fire dynamics simulator, and shows that the proposed solution is not only able to outperform comparable alternatives in static and dynamic environments, but also in environments with realistic spreading of fire and smoke causing fatalities. The aim of the solutions is usage by both individuals, such as from a personal smartphone of one of the evacuees, or for emergency personnel trying to assist large groups from remote locations.

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Notes

  1. SmartRescue: http://ciem.uia.no/project/smartrescue

  2. This is not to be confused with that hazard propagations are ignored. In fact, paper shows that even without the complicated hazard propagations, the learning algorithms learn and predict the hazards.

  3. Note that radiation in this paper refers to thermal radiation emitted by fire, and should not be confused with any other form of radiation such as nuclear radiation.

  4. Many additional experiments have been carried out. For reasons of brevity, only those more relevant are presented in this paper. Some of the additional experiment results are available in [23].

  5. Note that for the static environment the variants of h(v i ,t) used is h 1(v i ,t).

  6. An outline of the Thompson Spirit is available at http://www.iglucruise.com/thomson-spirit/deck-plans?deck=83.

  7. Two other simulations were carried out with similar setup but with fires starting in the corner of the first floor and the middle of the second floor. The results were similar to those shown in this paper — and are therefore left out.

  8. The ants do not have any knowledge of the spread of the fire other than what they observe through the function h(v i ,t), so “best” means most viable seen from the small windows available from the ants. In practice, data from fixed and mobile fire sensors will populate h(v i ,t).

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Authors and Affiliations

  1. Faculty of Engineering and Sciences, Department of ICT, Univesity of Agder, Grimstad, Norway

    Morten Goodwin, Ole-Christoffer Granmo & Jaziar Radianti

Authors
  1. Morten Goodwin
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  2. Ole-Christoffer Granmo
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  3. Jaziar Radianti
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Correspondence to Morten Goodwin.

Additional information

Preliminary versions of some of the results of this paper appeared in the Proceedings for 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013.

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Goodwin, M., Granmo, OC. & Radianti, J. Escape planning in realistic fire scenarios with Ant Colony Optimisation. Appl Intell 42, 24–35 (2015). https://doi.org/10.1007/s10489-014-0538-9

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