Flocking Behaviors

Presented by Jyh-Ming Lien

Flocking System

What is flocking system?– A system that simulates behaviors of accumulative

objects (e.g. a school of fishes, people crowds…)

Why do they form flocks?– Because they are selfish.

Why do we need flocks?

(http://www.actwin.com/fish/marine-pics)

Review : Particle System

Star Trek II, 1982Karl Sims,1988.(Melting, 1999)

www.reptilelabour.com/software/flow

(lava, 1999)

Application of Flock behaviors

Computer Graphics Virtual Reality Robotics Games Heuristic Algorithm Biology and ecology simulation.

Application : Computer Graphics

Hard to make animation by key-framing.

Application : VR

Shopping online

www.activeworlds.com

Zoom in Zoom in

Application : Robotics

RoboCup98

Robot Sheep Dog

Application : Games

StarCraft

Empire Earth(trailer)

WarCraft 2 Black & White

Real Time Strategy (RTS) Games

Application : Heuristic Algorithm

Ant Colony Optimization– Based on ant’s behavior– Ant deposit pheromone on the ground and other ants will smell

it and make decision.– Pheromone will evaporate.

Used to solve hard problem, like TSP, routing

S GPath A (5 sec)

Path B (10 sec)

Each ant need to go to the goal and go back to the start point

Basic Flock Behavior

Interactive particle system– Particle system has no interactions between particles

Local Information– No central control system (individual-based model)

3 simple Rules– Separation – Alignment– Cohesion

http://www.red3d.com/cwr/boids

Basic Flock Behavior : Separation

http://www.red3d.com/cwr/boids/

Basic Flock Behavior : Alignment

http://www.red3d.com/cwr/boids/

Basic Flock Behavior : Cohesion

http://www.red3d.com/cwr/boids/

Steering : more complex behaviors

Craig W. Reynolds, Steering Behaviors For Autonomous Characters, Game Developers Conference, 1999.

Leader followingwall following path following

Pursuit and Evasion Arrival Obstacle Avoidance

Problem with Basic flocking behavior

Always emergent behavior– only local information is used– No memory

most creatures are not so stupid. At least they usually have memory.

– Can’t do complex task, like searching.

Can we have more complex behaviors?– Yes! Using global knowledge.– However, we need to use it very carefully.

this means we don’t want our creature to be too smart No creature will have complete/perfect information about global information.

                     

Adaptive roadmap

Yes, of course, Road Map.– Encodes global information. (e.g. topology)– Facility to access global information.

Adaptive roadmap edges. Provides indirect and cheap way to

communicate between flockmates.

(inspired from ACO)

<Joint work with Burchan and Nancy>

Experiments using Adaptive Roadmap

We try to show that using global information encoded in roadmap can generate “good” complex behaviors.

Roadmaps are generated using MAPRM. Experiments including:

– Homing– Covering– Goal Searching– Shepherding (Burchan will talk this next time)

Application : Homing

Find a path from current position to goal A very simple case

– Showing that we can keep properties of basic flocking behaviors while new behaviors are added.

– Comparing with most popular approach (A* search) for this problem.

Go

Go

alal

Application : Homing : Movies

Roadmap Approach A* approach

Application : Homing : Data

- 40 flock members

-Environment size: 420 m * 420 m

-301obstacles (6 types)

-Simulation updated every 100ms

2005A*

255

# of local minima

R

Application : Covering

Mine sweeping : covering the environment. A point, p, is defined “covered” if p is inside

view ranges of one or more flock member. Similar ACO

Memory is a list of roadmap nodes that are visited by the boid.

Probability of each edge been selected based on its weight. Edge with Smaller weight has higher probability to be selected.

Application : Covering : Movie

Basic flocking behavior Behavior with perfect information Behavior with adaptive roadmap

Application : Covering : Data

- 50 flock members

-Environment size: 80 m *100 m

-Sensory range: 5 m

Scene overview

Application : Goal Searching

Search an unknown goal, and, once it’s found, all flock members should go there.

Probability of each edge been selected based on its weight. Edge with larger weight has higher probability to be selected.

Memory is a list of roadmap nodes that are visited by the boid.

Application : Goal Searching: Movie

Basic flocking behavior Behavior with perfect information Behavior with adaptive roadmap

Application : Goal Searching: Data

- 50 flock members

-Environment size: 80 m *100 m

-Sensory range: 5 m

Scene overview

Conclusion

Application of Flocking– CG– VR– Robotics– Game– Heuristic Algorithm

Using Adaptive Roadmap to generate more complex behaviors.– Homing– Covering– Goal Searching– Shepherding

Flocking is FUN.