Crowd and multi-agent simulation is the process of simulating large numbers of people, creatures, or other characters, each interacting in one environment. These actors are expected to move to their goals, interact with their environment, and respond to each other. Crowd simulations have many uses, including improving architectural planning, enhancing training environments and virtual realities, and driving artificially-intelligent (AI) characters in games and movies. Our group has worked on many problems in crowd simulation, including fast, guaranteed, collision avoidance, real-time path and motion planning, crowd flows, and directed behaviors. See also our related work in Motion and Path Planning for single and multiple robots or agents.
d-ORCA: Distributed Optimal Reciprocal Collision Avoidance, the first decentralized collision avoidance simulation package for quadrotor swarms. Users of the package can simulate a swarm of up to 50 quadrotors which can avoid collisions with each other and with static obstacles in a known environment, and easily add and test their own perception or global path planning code with the package.
Menge: A Modular Framework for Crowd Movement Simulation.
RVO2: Reciprocal Collision Avoidance for Real-Time Multi-Agent Simulation.
HRVO: Hybrid Reciprocal Velocity Obstacle.