Real-time simulation of crowds of heterogeneous agents

Behavioural simulation of agents representing humanoid characters has spread to many areas in recent years. A part of such simulations are crowd simulations, where large numbers of agents move and interact at the same time. Finding a suitable level of individual agent complexity so that large simulations are possible and suitable behaviour is reached, is challenging. In addition, executing such a simulation in real-time is problematic. In my work I developed a real-time application in Unity game engine which makes use of a number of main techniques and approaches for heterogeneous crowd simulations, such as modular architecture, environment sensing, obstacle avoidance, finite state machines for behaviour modeling, animator for animation visualisation etc. I thoroughly described and presented those approaches and techniques and commented on the results obtained in several different scenes which represent specific real-world situations

Grid-based partitioning for large-scale distributed agent-based crowd simulation

Agent-based crowd simulation, which aims to simulate large crowds of autonomous agents with realistic behavior, is a challenging but important problem. One key issue is scalability. Parallelism and distribution is an obvious approach to achieve scalability for agent-based crowd simulation. Parallel and distributed agent-based crowd simulation, however, introduces its own challenges, in particular, effectively distributing workload amongst multiple nodes with minimal overhead. In order to ensure effective distribution with low overhead, a proper partitioning mechanism is required. Generally, human crowds consist of groups or exhibit particular patterns of flow, which are then reflected in simulations. Exploiting this grouping with an appropriate partitioning mechanism should enable efficient distribution of crowd simulation. In this paper we introduce a grid-based clustering algorithm which we compare to previous clustering approaches that used the K-means algorithm