29,375 research outputs found
Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)
Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we
introduce a novel crowd simulation method that runs at interactive rates for
hundreds of thousands of agents. Our method enables the detailed modeling of
per-agent behavior in a Lagrangian formulation. We model short-range and
long-range collision avoidance to simulate both sparse and dense crowds. On the
particles representing agents, we formulate a set of positional constraints
that can be readily integrated into a standard PBD solver. We augment the
tentative particle motions with planning velocities to determine the preferred
velocities of agents, and project the positions onto the constraint manifold to
eliminate colliding configurations. The local short-range interaction is
represented with collision and frictional contact between agents, as in the
discrete simulation of granular materials. We incorporate a cohesion model for
modeling collective behaviors and propose a new constraint for dealing with
potential future collisions. Our new method is suitable for use in interactive
games.Comment: 9 page
Resilience and Controllability of Dynamic Collective Behaviors
The network paradigm is used to gain insight into the structural root causes
of the resilience of consensus in dynamic collective behaviors, and to analyze
the controllability of the swarm dynamics. Here we devise the dynamic signaling
network which is the information transfer channel underpinning the swarm
dynamics of the directed interagent connectivity based on a topological
neighborhood of interactions. The study of the connectedness of the swarm
signaling network reveals the profound relationship between group size and
number of interacting neighbors, which is found to be in good agreement with
field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl.
Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic
collective behaviors enabling us to uncover that the swarm signaling network is
a homogeneous clustered small-world network, thus facilitating emergent
outcomes if connectedness is maintained. Resilience of the emergent consensus
is tested by introducing exogenous environmental noise, which ultimately
stresses how deeply intertwined are the swarm dynamics in the physical and
network spaces. The availability of the signaling network allows us to
analytically establish for the first time the number of driver agents necessary
to fully control the swarm dynamics
An information theory based behavioral model for agent-based crowd simulations
Crowds must be simulated believable in terms of their appearance and behavior to improve a virtual environment’s realism. Due to the complex nature of human behavior, realistic behavior of agents in crowd simulations is still a challenging problem. In this paper, we propose a novel behavioral model which builds analytical maps to control agents’ behavior adaptively with agent-crowd interaction formulations. We introduce information theoretical concepts to construct analytical maps automatically. Our model can be integrated into crowd simulators and enhance their behavioral complexity. We made comparative analyses
of the presented behavior model with measured crowd data and two agent-based crowd simulators
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