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? (

Review : Particle System Star Trek II, 1982Karl Sims,1988. (Melting, 1999) (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 Zoom in

Application : Robotics RoboCup98 Robot Sheep Dog

Application : Games StarCraft Empire Earth (trailer) WarCraft 2Black & 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 SG Path 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

Basic Flock Behavior : Separation

Basic Flock Behavior : Alignment

Basic Flock Behavior : Cohesion

Steering : more complex behaviors Craig W. Reynolds, Steering Behaviors For Autonomous Characters, Game Developers Conference, Leader followingwall followingpath following Pursuit and EvasionArrivalObstacle 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)

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. Goal

Application : Homing : Movies Roadmap ApproachA* approach

Application : Homing : Data - 40 flock members -Environment size: 420 m * 420 m -301obstacles (6 types) -Simulation updated every 100ms 2005 A* 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 behaviorBehavior with perfect informationBehavior 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 behaviorBehavior with perfect informationBehavior 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.