Editing Crowd simulation (section)

=== Algorithm by Patil and Van Den Berg ===
This algorithm was designed for relatively simplistic crowds, where each agent in the crowd only desires to get to its own goal destination while also avoiding obstacles.<ref>{{cite journal |doi=10.1109/TVCG.2010.33 |pmid=21149879 |title=Directing Crowd Simulations Using Navigation Fields |journal=IEEE Transactions on Visualization and Computer Graphics |volume=17 |issue=2 |pages=244–54 |year=2011 |last1=Patil |first1=Sachin |last2=Van Den Berg |first2=Jur |author-link2= Jur P. van den Berg|last3=Curtis |first3=Sean |last4=Lin |first4=Ming C |last5=Manocha |first5=Dinesh }}</ref> This algorithm could be used for simulating a crowd in Times Square.

Patils algorithm's most important and distinctive feature is that it utilizes the concept of ''navigation fields'' for directing agents. This is different from a guidance field; a guidance field is an area around the agent in which the agent is capable of "seeing"/detecting information. Guidance fields are typically used for avoiding obstacles, dynamic obstacles (obstacles that move) in particular. Every agent possesses its own guidance field. A navigation field, on the other hand, is a vector field which calculates the minimum cost path for every agent so that every agent arrives at its own goal position.

The navigation field can only be used properly when a path exists from every free (non-obstacle) position in the environment to one of the goal positions. The navigation field is computed using coordinates of the static objects in the environment, goal positions for each agent, and the guidance field for each agent. In order to guarantee that every agent reaches its own goal the navigation field must be free of local minima, except for the presence of sinks at the specified goals.

the algorithm is only dependent on the grid resolution and not dependent on the number of agents in the environment. However, this algorithm has a high memory cost.