  Emergency navigation algorithms direct evacuees to exits when disastrous
events such as fire take place. Due to the spread of hazards, latency in
information updating and unstable flows of civilians, emergency evacuation is
absolutely a complex transshipment problem involving numerous sources and
multiple destinations. Previous algorithms which commonly need either a full
graph search or a convergence process suffer from high computational and
communication overheads. This research report surveys the current emergency
navigation algorithms and adapts the concept of Cognitive Packet Network (CPN)
to the context of emergency evacuation. By using random neural networks, the
CPN based algorithm can explore optimal routes rapidly and adaptively in a
highly dynamic emergency environment with low expense. Simultaneously, in
emergency situations there are typically different categories of evacuees such
as people of different age groups. However, current algorithms only consider
"normal" evacuees and do not meet the specific requirements of diverse
evacuees. Our algorithms make use of the flexibility of CPN which can operate
with different user-defined goals to customize appropriate paths for each
category. The CPN algorithm is simulated in a graph based discrete-event
simulator and Dijkstra's shortest path algorithm is taken as reference. The
results show that the CPN algorithm reaches the performance of ideal
path-finding algorithm and quality of service is improved by using specific
goal functions for diverse categories of evacuees. Finally, we present a future
plan for further research.
