Group evasion
behavior is the evasion pattern of numerous agents, one kind of the most
frequently observed behavior patterns in nature. However, there appears to be
rather little research about human crowd evasion behavior. In this paper, I
introduce a new model for simulating group evasion behavior based on biological
and sociological models for the purposes of simulating the crowd animation in
evasion situations. In biology, fish school group evasion behavior is well
studied and it has a possibility that the human crowd evasion behavior in
emergency is similar to fish school group evasion patterns. I take this
biologically inspired model and seek to extend it by integrating sociological
factors found in human groups. For the immediate dangers, bio-inspired model
could simulate human evasion movement. With sociological factors, I can
simulate more complex evasion patterns with considering sociological factors.
With this model, I would be able to simulate crowd evasion behavior in
emergency situations. Introduction: The collective behavior of human crowds is
one of the most interesting topics for numerous researchers and research areas
such as sociology, virtual reality, computer graphics, robotics, psychology,
politics, transportation, etc. However, it is difficult to simulate crowd
behavior with a good mathematical model because many factors influence how each
individual will behave and affects the overall crowd behavior. The factors that
induce a variation of a person’s behavior are physical attributes, personality,
social status, relations with other individuals, etc. These factors make
scientific experiments very hard, and generate difficulties for validation.
There are various models to explain human crowd behavior in sociology and
psychology [27, 28, 29, 32], but none of them is proven because of the
previously mentioned difficulty in the modeling and the validation. Moreover,
we cannot experiment on human crowds to produce collective evasion patterns, so
it is difficult to retrieve relevant data. Also, collective evasion patterns
are usually expressed in emergency situations, so few pieces of video data for
this behavior are available. 1. Previous works in crowd simulation and motion
planning Crowd simulation and multi-agent motion planning have been studied by
many researchers. One of the main research topics in robotics is to enable
multi-robot agents to navigate autonomously and collision-free in various
environments. In computer graphics, motion planning with autonomous agents
could be used for digital actors, which can react and adapt to high level
directives in dynamic environments. Navigating in dynamic environments [2, 3,
4, 6, 8] and efficient collision avoidance [1, 5] for numerous agents are main
topics for multi-agent motion planning. Crowd simulation is very difficult not
only for the multi-agent motion planning problem, but for the inherent
complexity in the behavior of each human individual. For the purpose of
handling very large number of agents, there are two major approaches for crowd
simulation modeling; considering the crowd as particles or considering the
crowd as a large group of agents. The particle approach for the crowd modeling
is based on a continuum perspective for the crowd, which considers crowd as
some continuous material. It considers crowd motions as per-particle energy
minimization [5, 8, 14]. Because the system controls all the particles
entirely, all the individual motions could be guided easily by global motion
planning. Also, it is quite efficient for computation, because increasing the
number of agents is just condensing the density of particles. However, because
it is based on continuum perspective, the motions of individuals are similar to
fluid and not quite realistic [5, 14]. The multi-agent approach for crowd modeling
is much more complex than the particle approach. The complexity of the
multi-agent motion planning increases exponentially with the number of agents
and their degree of freedom. However, with this model, each agent can react
with some intelligence, similar to the real world, so we can control each
agent’s movement more precisely with more details and with autonomous
navigation capabilities [2, 3, 4, 6, 8]. Within various group behaviors, the
crowd evasion behavior is useful for numerous applications: animation,
simulation, social science, virtual reality for various situations: evacuation,
police chasing to arrest the riot or protester situation, minimizing damage in
terror situation, escaped animals from zoo, controlling disorder situation, or
video games. There is some good research on crowd evasion situations evacuation
[9], and suicide bombing terrorist [11]. Helbing presents collective crowd
behavior of evacuation, induced by panic, using a simulation based on a social
force model. He considered the social force with psychology information, and
simulates group evasion patterns resulting jamming in life-threatening
situations. Zeeshan shows that running to an exit exposes a person to death
threatening situation when the suicide bomber attacks the crowd. However, we
are not aware of any works in crowd simulation about use of evasion behaviors
based on biological models. 2. Bio-inspired algorithms It is a well developed
approach to introduce biological algorithms in various fields: optimization, robotics,
networking, social organization, etc. In particular, robotics researchers are
mainly focused on an emergent behavior of a biological swarm, which is a
high-level goal-driven group behavior resulting from the cooperation of simple
individual patterns. Emergent group behavior patterns are easily found as
insect swarm and animal herd behaviors in nature. Biological individuals only
have limited sensing capability, and use simple and robust decentralized
algorithms. Biological algorithms are easily applied to simple autonomous
robots for these properties, so there are numerous bio-inspired research
studies in robotics [17, 18, 20, 23, 24]. Because the biological algorithms or
procedures have survived the evolutionary process, they are shown by natural selection
to be effective, robust and efficient. Bio-inspired approaches are often used
when dealing with overly complex problems, or when there exist similar problems
in nature. Many researchers have used biological inspirations. Reynolds [15]
simulates the flocking behavior inspired by flock of birds. Svennebring and
Koenig introduce ant pheromones for their terrain covering robots. Schwager et
al. bring in the ladybugs’ algorithm to solve terrain coverage problem with
distributed agents [20]. Halasz et al. introduce the quorum sensing for the
redistribution of swarm robots, which is inspired by ants’ house hunting
algorithm [18]. Stafford et al. utilize the locust vision system in the
collision detection mechanism for cars [24]. Barrows adopts the optic flow to
UAV flight control, which is used by insects’ vision to avoid collision [23].
The genetic algorithm and the ant colony optimization are used in numerous
fields: game theories, NP complete optimization problems, etc [19]. However,
there are few papers [15] in computer animations for group behaviors which are
biologically inspired. In emergency situations, human crowd reactions are
similar to animal group behavior in a high level perspective, because of the
urgency to react – “Individuals start pushing, and interactions among people
become physical in nature” [9]. The reactions of people vary at the different
levels of danger and situations [29]. However, with immediate dangers for
primitive sensors, people usually have no time to think about the complex environmental
information, so I believe that there should be some linkages between the crowd
evasion behavior and the animal group evasion behavior. Therefore, adopting
animal group evasion models should be useful to simulate the crowd evasion
patterns. Animal group evasion behaviors are well studied because it is one of
the most important behaviors for animal researchers. Unlike the human crowd
evasion behaviors cases, the experiment for the animal group evasion behaviors
easily controls variables, and generates repeatable situations. Therefore,
animal researchers have developed good mathematical models for animal group
evasion cases. Especially, fish group evasion patterns are one of the best
developed behaviors and have some good mathematical models with relevant
evidence data [21, 22]. Among the fish group evasion behavior models, Inada et
al. [21] suggest a good mathematical model with the validation of simulation
and proper proof. Its basic model is based on Aoki’s and Huth & Wiseel’s
model. This model is based on individual behaviors, so it is proper to generate
emergent behaviors. It is two-dimensional model based on velocity and angular
change with decision time delay also being considered. The decisions of fish
individuals are based on motions of neighborhood, with some tendency value. The
basic strategy for grouping is similar to Reynold’s flocking [15]. Fish
individuals move during building a group with motions of approaching, parallel
orientation, and repulsion. With this model, the authors could simulate all
reported evasion patterns of real fish schools, except for the ball pattern. It
is a good model with reliable validation data, but has some limitations. Their
model only considers free field with no obstacles, and only one predator. Also,
they do not think about the factor of domains of danger [25] or the speed
change for urgent evasions.