1. Behavioral animation
CSE 3541
Matt Boggus

2. Material recap and trajectory
Geometric
Artist specifies translation and rotation over time
Physically based
Artist specifies forces acting on objects
Motion equations dictate movement
Behavioral
Artist directs autonomous agents
Interpolation
Artist draws the scene at the beginning and end of an interval of time
Intermediate positions are calculated

3. Behavioral Animation
Control the motion of one or more objects using virtual actors
Goal: realistic or believable motion so that the object appears to be autonomous
Matt Lewis’ page on BA
Example videos 1 + 5

4. Actor properties (position only)
Goal (xg, yg)
V = Goal - Position
Force based
Next Position = Position + V * dt
Position (x, y)

5. Problems with repulsive forces

6. A hierarchy of motion behaviors
Action selection
State logic
Reflex agent
Steering
Position and orientation changing logic
Locomotion
Figure animation covered later in 3541
Keep using primitive shapes or non-animated meshes
Figure source: Steering Behaviors For Autonomous Characters

7. Reflex agent
Figure source:

8. Reflex agent – predator

9. Actor properties (position and orientation)
Orientation (ox, oy) = transform.forward
Rotate(θ)
Determine which rotation (±θ) orients the actor closer to the goal using dot product
Rotate(-θ)
Goal (xg, yg)
Vtarget = Goal - Position
Steering based
Position (x, y)
In Unity, you can use RotateTowards()

10. Orientation vectors in Unity
Image
Image source:

11. Sample starter code
class OrientedAgent2D { // Data GameObject model; // Use this field for geometry, // position, and orientation // Methods Update(float deltaTime); TurnLeft(); TurnRight(); MoveForward(); MoveBackward(); };
For an agent moving in the x-z plane, its y position is and y component of transform.forward are both always zero

12. Behavioral animation Character or object motion based on:
Knowledge of the environment
Aggregate behavior
Primitive behavior
(Intelligent behavior)
(Crowd management)

13. Knowing the environment
Vision and other senses
Information available from the current state of the scene
Computed “fresh” each Update()
Memory
Information stored from the past
Ex: lastKnownPlayerPosition field set at the end of an Update call where an enemy ai saw the player

14. Lab4 design note
Predator class/objects/script must be able to access prey class/objects/script in order to get position for vision test
Multiple options
All objects are fields in one script
Or, add methods that pass/take references to other objects or find objects by name

15. Vision Targeted vision General vision
Can the actor see object X?
Boolean CanSee(object X);
General vision
What is everything the actor can currently see?
Brute force approach: loop over all objects, use targeted vision on each one, return arrayList of GameObjects “seen”
Possible computation/accuracy tradeoff
How much of an object needs to be seen to be identified?
Do we need to model visual perception?

16. Omniscience Everything in the scene is known
CanSee(object X) { return true };

17. Distance limited omniscience

18. Field of view

19. Field of view – example Orientation Vector (O)
Vector from agent to vertex (V)
The dot product logic doesn’t take into account occlusion.
Remember to normalize V before doing dot product.
If the agent cannot see far away, also check that the magnitude of the V vector doesn’t exceed the max distance it can see.
Angle of cone = θ
Inside the cone when the angle between O and V is less then or equal to θ/2

20. Field of view – sample code
Vector3 agentPosition, orientation, objectPosition; float visionLimit; Vector3 agentToVertex = objectPosition – agentPosition; agentToVertex.Normalize(); if(Vector3.Dot(agentToVertex,orientation) > visionLimit) // objectPosition is in cone

21. Occluded Vision Ray casting with collision detection
Sample the environment

22. How well can the actor see X?
Target-testing vision (per object)
How well can the actor see X?
Use multiple rays
Can the actor see X?
Cast a ray

23. Target-testing vision (alternative)
Sample the vision cone
Cast multiple rays

24. Other senses Hearing Smell Sensors & signal propagation
Spatial occupancy approach
Applications

25. Spatial Occupancy

26. Memory What is recorded about the environment Spatial occupancy
Transience of objects: time-stamps
Memory hierarchy: short-term, long-term

27. Behavioral animation Character or object motion based on:
Knowledge of the environment
Aggregate behavior
Primitive behavior
Predator-Prey forces and movement
Predator-Prey steering
Intelligent behavior
Crowd management

28. Lab4 – predator-prey simulation
Unbalanced abilities
Vision
Distance
Field of view
Linear speed (moving forward)
Angular or rotational speed (turning)
(For stealth game concept, consider enemy AI agents as predators, player controls prey)

29. Predator Prey vision anatomy

30. Prey-Predator (vision)

31. Prey-Predator
(movement – speed and turning)

32. Motion – force based Force = c * posprey - pospred
Apply a force on the predator towards the prey when in view

33. Motion – kinematic based
vnew
vold
Determine the closest prey in view
then turn towards it and increase forward velocity

34. Prey-Predator – environment forces
Using pure forces
May not prevent object penetration
Prey can be ‘hidden’ by environmental repulsive forces

35. Behavioral animation Character or object motion based on:
Knowledge of the environment
Aggregate behavior
Emergence
Primitive behavior
Intelligent behavior
Crowd management

36. Aggregate Behavior: Emergent Behavior Type Elements Physics Env/Others
Typical qualities
Type
Elements
Physics
Env/Others
Intelligence
Particles
Much/none
None
Flocking
Some/some
Limited
Crowds
Little/much
Little-much

37. Emergent behavior
Complex systems and patterns arising out of simple rules
Aints – AI ants

38. Flocking, Swarming, Flying
Boids

39. Local control
Rules defined to stay with friends, but avoid bumping into each other
Need additional rules for collision avoidance with obstacles

40. Local information

41. Other flocking issues Global control Negotiating the motion
script flock leader
global migratory urge
Negotiating the motion
Separation, alignment, and cohesion may compete/contradict
Collision avoidance
Once an object is in sight, start steering to avoid
Splitting and rejoining (difficult)
Collision avoidance too strong – flock may never rejoin after split
Flock membership too strong – flock does not split and formation changes
Modeling flight

42. Navigating using bounding sphere

43. Testing for being on a collision path with (bounding) sphere
Navigating
Testing for being on a collision path with (bounding) sphere
Given: P, V, C, r

44. Finding closest non-colliding point
Calculate s,t

45. Additional Slides, time permitting

46. Spatial Occupancy
transiency
doorway
doorway
wall

47. Negotiating the motion
Forces Or
“Reasoning”
(e.g. rule-based)

48. Modeling flight – common in flocking

49. Modeling flight
Geometric flight – dynamic, incremental rigid transformation of an object moving along a tangent to a three dimensional curve

50. Less pressure above wing = lift
Modeling Flight – Lift
Air passing above wing most travel longer than air below it.
Less pressure above wing = lift

51. Modeling Flight – turn by rolling

52. Modeling Flight – summary
Turning is effected by horizontal lift
Increasing pitch increases drag
Increasing speed increases lift