a crowd simulation using individual- knowledge-merge based path construction and smoothed particle...

A Crowd Simulation Using Individual-Knowledge-Merge based Path Construction and Smoothed Particle Hydrodynamics

Weerawat Tantisiriwat, Arisara Sumleeon and Pizzanu Kanongchaiyos

Dept. of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand.

Outline

• Introduction

• Literature Review

• Objective

• Individual-Knowledge-Merge Method

• Conclusion & Future Works

Introduction

• The current crowd simulation consist of 2 stepsFirst :

Prepare the global path construction to go to the destination.

Second :

Simulate the crowd locomotion along with the created path by using behavioral rules.

Outline

• Introduction


• Objective



Related Works(1/4)

Autonomous pedestrians [Wei et al., 2005]

Able to analyze the situation from the perceiving environment.

х Unable to demonstrate natural crowd locomotion.

Related Works(2/4)

Continuum Crowds [Treuille et al., 2006]

Able to demonstrate unfixed-pattern of crowd locomotion.

хUnable to analyze the situation from surrounding environment.

хUnable to automatically find the destination.

Related Works(3/4)

Continuum Crowds [Treuille et al., 2006]

Able to generate the locomotion direction in the all position.

Able to avoid the obstacle and another individual automatically.

х Unable to generate potential if do not specify the destination.

Related Works(4/4)

• The results : Interactive time simulation. Natural phenomena locomotion demonstration.

• The problems :Unable to simulate crowd behavior for finding the destination in unknown environment.

Unable to construct the path if do not use the global map knowledge.

Outline

• Introduction


• Objective



Objective

• To simulate crowd behavior for finding the destination in the unknown environment.

• To simulate unfixed-pattern of crowd locomotion.

Outline

• Introduction


• Objective



Implementation

Begin

Create agent’s vision or receive map-knowledge from neighbor agents

(Perception)

Create/Merge a new map-knowledge(Recognition)

Does the map-knowledge have major

destination?

Generate a path to go to destination in the map-knowledge by using potential field method

(Decision)

Select minor destinations (connection area) to replace the major destination

Follow the direction that is calculated by Smooth Particle Hydrodynamics

(Locomotion)

Does agent arrive at the destination?

DisplayStop

No

Yes

No

Yes

Individual-Knowledge-Merge(1/9)

This method is used to simulate crowd behavior and crowd locomotion for finding the destination in unknown environment by using local map knowledge.

Consist of :

- Perception

- Recognition

- Decision

- Locomotion


PerceptionTo perceive the data in the environment by shooting a ray.

- Vision - Communication


Recognition

To create local map knowledge by recognizing from perception.


DecisionTo select a appropriate path from generated potential to go the destination.

Case 1 : The destination is in the local map knowledge.

Case 2 : The destination does not be in the local map knowledge.


Decision : The destination is in the map

= Distance

= Density

= Convenience

Sg


Decision : The destination does not be in the map

The connection area is became a minor destination


LocomotionTo calculate next position by using computational fluid dynamics.

fvpDt

vD

21

exti

visci

pressureii ffff

),()( hrrWm

ArA jij

j

Njjs

),()( hrrWA

mrA jj

j

Njjs

),()( 22 hrrWA

mrA jj

j

Njjs

Smooth Particle Hydrodynamics

SPHs is an interpolation method that approximates the value of a continuous field quantity and its derivative by using discrete sample points.

{


Locomotion),(

2hrrW

ppmf ji

Nj j

jij

pressurei

Pressure force External body force Viscous force

),(2 hrrWvv

mf jiNj j

ijj

visci

extif

= Potential force field


Locomotionexti

visci

pressureii ffff

Outline

• Introduction


• Objective



Conclusion & Future Works

Conclusion

This system can use for crowd simulation by • Able to find the destination in the unknown environment

automatically.• Able to demonstrate unfixed-pattern of crowd locomotion.

Future Works• Improve the behavioral model.• Improve the decision factors.