Upload
aleda
View
27
Download
1
Embed Size (px)
DESCRIPTION
Crowd Self-Organization, Streaming and Short Path Smoothing. Stylianou Soteris & Chrysanthou Yiorgos. 學號: 9555535 姓名:邱欣怡 日期: 2007/1/2. Outline. Introduction Related work Method & Algorithm Result Performance Conclusion. Introduction. - PowerPoint PPT Presentation
Citation preview
Crowd Self-Organization, Streaming and Short Path Smoothing
學號: 9555535姓名:邱欣怡日期: 2007/1/2
Stylianou Soteris & Chrysanthou Yiorgos
Outline
Introduction Related work Method & Algorithm Result Performance Conclusion
Introduction
Collective behavior of pedestrians, known as self-organization
Flow grid mechanism in introduced to simplify navigation and enable crowd self organization
Related Work
Crowd navigation Deal with the problem of steering an
avatar inside a large amount of static and moving obstacles
Related Work (conti.)
Approaches to solving the navigation problem
1. Path planning method Not good for dense crowd navigation
2. Reactive navigation method Significant work
3. Behavioral navigation method Simulate aspects of pedestrian behavior
Related Work (conti.)
Reactive navigation method1. Force field method
Collision avoidance and smooth steering2. Rule based method
Model complex behaviors through a combination of attributes and rule, and through FSM
3. XZT space method Using space-time represent movements of d
ynamics objects
Methodology
First ,flow grid is constructed over the walk area Flow grid->measure densities and
velocities at various directions Using flow grid to navigate Using Steering algorithm to local
steering and smoothing
Measuring the Flows
Each avatar is registered on the grid by distributing his density and velocity to the 4 neighboring points
Velocities are separated into X and Z axis components and stored at each point, (+vx,-vx,+vz,-vz)
Complete Picture
Using the Flow Grid to Navigate
Weight = (1+D) * (1+AngleDiff(T,F)) D = density at spot T = vector showing direction towards target pos F = vector showing direction of flow at spot AngleDiff =Angle difference between 2 vectors in radians
Steering Algorithm
Using discrete occupancy map
Actual avatar First 4 position are used for curve interpolation (Catmul-Rom curve)
Last 2 position are used for path smoothing (in order to minimize the turn angle)
Next position search
Search starts from the top and checks left and right at an increasing angle and reducing distance until an empty cell found
Result
Two Parallel But Opposing Streams
Result
Two Crossing Crowd Streams
Result
Resulting paths of parallel but opposing streams
Resulting paths of perpendicularly crossing streams
Performance
Improved Density of avatars. Approximate crowd jam limits for different area sizes
Performance
Local Navigation performance cost
Conclusion
Flow grid gives an overall impression of what kind of pedestrian traffic exists in that area, and it can detect Congested araes.
Steering algorithm can reduce the collisions avoidance cost.