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Published in:
2013 | OriginalPaper | Chapter
Ant Colony Optimisation for Planning Safe Escape Routes
Authors : Morten Goodwin, Ole-Christoffer Granmo, Jaziar Radianti, Parvaneh Sarshar, Sondre Glimsdal
Published in: Recent Trends in Applied Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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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 a predefined escape route is 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 route 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 both dynamic spread of hazards and congestion avoidance.
The solution can be used both on an individual bases, such as from a personal smart phone of one of the evacuees, or from a remote location by emergency personnel trying to assist large groups.