Um modelo híbrido para simulação de multidão com comportamentos variados em tempo real

Abstract

Crowd simulation is a computationally expensive task, where there is the need to reproduce the behavior of many (tens to thousands) agents in a two-dimensional or three-dimensional environment realistically. The agents need to interact to each other and with the environment, reacting to situations, alternating behaviors and/or learning new behaviors during his “lifetime”. Many models to simulate crowds have been developed over the years and can be classified into two big groups (macroscopic and microscopic) according to how the agents are managed. There are some works in the literature based on macroscopic models, where the agents are grouped and guided by the potential field of their group. The construction of these fields is the bottleneck of these models, so it is necessary to use few groups if it is needed for a simulation to run at interactive frame rates. In this work is proposed a model based on a macroscopic model, which aims mainly to reduce the cost of calculating the potential fields of the groups, by using groups discretized according to the needs of the environment. At the same time it is proposed the addition of groups that can steer the agents of a simulation to momentary goals, which gives the crowd a wider variety of behaviors. Finally, it is proposed the use of a social forces model to prevent collisions between agents and between agents and obstacles.