behavior. autonomous characters acknowledgement much of this material is taken from the work of...
TRANSCRIPT
Autonomous Characters
AcknowledgementMuch of this material is taken from the work of Craig Reynolds. He maintains a web pages including a rich source of material of steering behavior and the consumate source on flocking.
Also see: Steering Behaviors For Autonomous Characters
by Craig Reynolds
Autonomous Characters
Self-Directed characters "puppets that pull their own strings" -Ann Marion Situated Live in a world shared by other entities Embodied Physical manifestation (virtual) Reactive
instinctive, driven by stimulus Improvisation, life-like behavior
Emergent Behavior
The appearance of consistent global behavior from a set of local rules enforcing independent constraints.
Emergent group behavior is the appearance of coordinated collective behavior of many individuals from individual behaviors based on independent, local interactions.
Emergent Misbehavior?
Permits modular development of complex behaviors
Hard to predict interactions among rules• Sometimes surprising and undesirable behaviors
appear in new circumstances or when new rules are added.
• Hard to debug.
Three-Tier Hierarchy
Action selectiongoals and strategies “What to do”
Steeringguidance / motion control “How to do it”
Locomotionmovement generation “Getting it done”
Cowboy Analogy
Action selection Trail boss: “Fetch that stray.”
SteeringCowboy: “Giddy-up, that away.”
LocomotionHorse “Wilbur!”
Flocks in Film
1987: Stanley and Stella in: Breaking the Ice, (short) Director: Larry Malone, Producer: Symbolics, Inc.
1988: Behave, (short) Produced and directed by Rebecca Allen
1989: The Little Death, (short) Director: Matt Elson, Producer: Symbolics, Inc.
1992: Batman Returns, (feature) Director: Tim Burton, Producer: Warner Brothers
1993: Cliffhanger, (feature) Director: Renny Harlin, Producer: Carolco.
1994: The Lion King, (feature) Director: Allers / Minkoff, Producer: Disney.
Flocks in Film
1996: From Dusk Till Dawn, (feature) Director: Robert Rodriguez, Producer: Miramax
1996: The Hunchback of Notre Dame, (feature) Director: Trousdale / Wise, Producer: Disney.
1997: Hercules, (feature) Director: Clements / Musker, Producer: Disney.
1997: Spawn, (feature) Director: Dipp₫, Producer: Disney.
1997: Starship Troopers, (feature) Director: Verhoeven, Producer: Tristar Pictures.
1998: Mulan, (feature) Director: Bancroft/Cook, Producer: Disney.
Flocks in Film
1998: Antz, (feature) Director: Darnell/Guterman/Johnson, Producer: DreamWorks/PDI.
1998: A Bugs Life, (feature) Director: Lasseter/Stanton, Producer: Disney/Pixar.
1998: The Prince of Egypt, (feature) Director: Chapman/Hickner/Wells, Producer: DreamWorks.
1999: Star Wars: Episode I--The Phantom Menace, (feature) Director: Lucas, Producer: Lucasfilm.
2000: Lord of the Rings: the Fellowship of the Ring (feature) Director: Jackson, Producer: New Line Cinema.
Motor Control
Steering Force
Integrate to determine acceleration Thrust – determines speed Lateral Steering Force – determines direction
Boid Object Representation
Point Mass Vehicle Mass Position Velocity Orientation
Constrained to align with velocity Force and Speed Limits
(No moment of intertia)
Euler Integration
acceleration = steering_force / mass
velocity = velocity + acceleration
position = position + velocity
Seeking and Fleeing
Aim towards targetDesired_velocity = Kp (position – target)
Steering = desired_velocity – velocity
Seeking and Fleeing Applet (Reynolds)
Pursuing and Avoiding
Target is another moving object Predict target’s future position Scale prediction time, T, based on distance to object, Dc
T=Dc
Pursuing and avoiding applet (Reynolds)
More Behaviors
Evasion Like flee, but predict pursuer’s movement
Arrival Like seek, but step at target Applet (Reynolds)
Obstacle Avoidance1. Repulsive force2. Aim to boundary3. Adjust velocity to be perpendicular to surface
normal
Leader Following
Based on arrival Target is behind leader
Clear leader’s front Separation avoids
crowding Applet (Reynolds)
Arbitration of Competing Demands
1. State Machines Context dependent selection Problem: combinatorial explosion
2. Winner Take All Choose highest priority goal Problems: dithering, fairness, and tunnel vision
3. Blending Combine output (e.g. sum, average, min, …) Problem: combination may satisfy no one
Flocking Demos
Flocking Applet (Craig Reynolds) Fish Schooling (Steve Hughes) Beach House (Ishihama Yoshiaki )
For more demos see Reynolds “Boids in Java”
Do People Flock?
Social psychologist’s report the people tend to travel as singles or in groups of size 2 to 5.
“Controlling Steering Behavior for Small Groups of Pedestrians in Virtual Urban Environments”
Terry Hostetler, Phd dissertation, 2002
Characteristics of Small Groups
Proximity Coupled Behavior Common Purpose Relationship Between
Members
Locomotion Model for Walking
Two Parameters Acceleration
Increase/reduce walking speed Combination of step length and step rate
Turn Adjust orientation Heading direction for forward walking
Accelerate Accelerate Accelerate Turn Left No Turn Turn Right
Coast Coast Coast Turn Left No Turn Turn Right
Decelerate Decelerate Decelerate Turn Left No Turn Turn Right
Action Space
Distributed Preference Voting
Seek best compromise through democratic voting Delegation of voters: Constraint Proxies
Proxies vote on every possible value of control variable (Weighed) votes are tallied
“Some citizens are more equal than others”
(Who said life was fair?) Winning cell represents best compromise
Bias towards incumbents to reduce dithering
(Now this is REAL politics)
Vote Tabulation
1.0
Pursuit Point
Tracking
Maintain Formation
Inertia
Centering
Maintain Target
Velocity
Avoid Peds
Winning Cell
Electioneer
1.0
1.0
2.0
2.0
4.0
5.0
Avoid Obstacles
A Group of Two Following a Path
ped 1
walkway axis
pursuit point
Winning vote = Accelerate/Turn Right
ped 2
-1.0 -1.0 +1.0-1.0 -1.0 +1.0-1.0 -1.0 +1.0
Pursuit Point Tracking
+1.0 +1.0 +1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0
Maintain Formation
+1.0 +1.0 +3.0 -3.0 -3.0 -1.0 -3.0 -3.0 -3.0
2.01.0
Election for ped 1
Avoiding an Obstacle -- Trajectory
Small look-ahead distance Large look-ahead distance
ped 1
ped 2
walkway axis walkway axis
ped 1
ped 2
Motion Control Through Optimization
Space-Time Constraints a great place to start is the Witkin and Kass SIGGRAPH paper
Spacetime Constraints
Andrew Witkin and Michael Kass,
SIGGRAPH, V. 22, N. 4, pp. 159-168, 1988.
(See me for class notes)