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Crowd simulation is becoming an essential part of computer sciences. In the last decades the research field has taken a flight. Current state-of-the-art micro simulation algorithms for crowd simulation are able to deliver very convincing results. Recently, vision-based algorithms have been developed that are based on the field of vision of individual agents. These algorithms allow agents to interact realistically at low to medium densities, but have trouble coordinating movement at high density. A concurrent development are flow algorithms. These algorithms are able to solve high density scenarios due to an inherent high level of coordination, but lack individuality of agents.

Up till now, however, no algorithm exists that can handle both low densities as well as extremely high densities realistically. In this thesis we will define an algorithm that can handle the entire density spectrum. Agents are individual entities with personal goals and settings. The algorithm will incorporate the implicit, local coordination present in real-life crowds.