Path planning refers to the computational problem of determining an optimal or feasible route for one or more agents to travel from a starting position to a goal location within an environment, while avoiding obstacles and adhering to movement constraints. In the context of crowd simulation and modeling, it is a foundational component that governs how individual agents or groups navigate shared spaces, directly influencing the realism and accuracy of simulated crowd dynamics. Key variants include global path planning, which computes full trajectories in advance using environment maps, and local path planning, which allows agents to make real-time adjustments in response to dynamic obstacles such as other pedestrians, with hybrid approaches increasingly common in data-driven frameworks that learn navigation strategies from observed human trajectory data.
Source Papers
- A review on crowd simulation and modeling ↗ — A review on crowd simulation and modeling
- Continuum theory for pedestrian traffic flow: Local route choice modelling and its implications ↗ — Continuum theory for pedestrian traffic flow: Local route ch
- Data-driven Crowd Modeling Techniques: A Survey ↗ — Data-driven Crowd Modeling Techniques: A Survey