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CSCI 4310

CSCI 4310. Lecture 5: Steering Behaviors in Raven. Book. Buckland Chapter 3, 7 Generating Automated Agent Behavior. High Level. Some ‘meta’ intelligence decides it is time to flee Ex: Rule: if badguy.size > me.size How do we implement fleeing?. Background. Vectors. Vector v(6,2).

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CSCI 4310

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  1. CSCI 4310 Lecture 5: Steering Behaviors in Raven

  2. Book • Buckland • Chapter 3, 7 • Generating Automated Agent Behavior

  3. High Level • Some ‘meta’ intelligence decides it is time to flee • Ex: Rule: if badguy.size > me.size • How do we implement fleeing?

  4. Background • Vectors Vector v(6,2) v(6,2) Magnitude = √(6² + 2²) = |v| = 6.32 Also called norm

  5. Background • Normalize Vectors New Magnitude = 1 Direction unchanged xN = x / |v| xN = 6 / 6.32 yN = y / |v| yN = 2 / 6.32

  6. Background Component Vectors v y Θ x a cos(Θ) = ay / |v| Θ = arccos (ay / |v|) ay = |v| cos (Θ) sin(Θ) = ax / |v| Θ = arcsin (ax / |v|) ax = |v| sin (Θ)

  7. Background Dot product of 2 vectors u, v u · v = uxvx + uyvy Dot product v y Θ x a Dot product of 2 vectors u, v u · v = |u||v| cos (Θ) normalize u, v u · v = cos (Θ) Θ = arccos (u · v) Angle between vectors

  8. Raven Vector2D struct has methods for Normalizing, Dot product, and adding, multiplying, etc. vectors

  9. Steering: Seek • Direct our player toward a target position • Return a force (Vector) to a target position • Airplane navigation

  10. Steering: Seek current target desired Steering force (seek) Desired = targetPos – vehiclePos Assume vehicle at (0,0) Desired = (5,6) Current + = Desired

  11. Steering: Seek • Vector2D SteeringBehaviors:: Seek(Vector2D TargetPos) • Vehicles contain a reference to a SteeringBehaviors • SteeringBehaviors::Seek returns a Vector2D • Returned Vector2D represents the steering force • Seek demo

  12. Steering: Flee • Run away from a given position • Return a force (Vector) to a target position • Opposite of seek • Modified in book p. 92 to flee only when a target is within a certain range

  13. Steering: Flee current target Steering force (flee) desired Desired = vehiclePos – targetPos Assume vehicle at (0,0) Desired = (-5,-6) Current + = Desired

  14. Steering: Flee • Vector2D SteeringBehaviors:: Flee(Vector2D TargetPos) • Returned Vector2D represents the steering force • Flee demo

  15. Steering: Arrive • Vector2D SteeringBehaviors:: Arrive(Vector2D TargetPos, Deceleration decel) • Arrive allows you to seek and come to a slower arrival (based on deceleration parameter) • Deceleration is an enumeration, slow=3, normal=2, fast=1

  16. Steering: Arrive • Just adjust speed multiplier (magnitude) of DesiredVelocity vector to slow down • Base on distance to target • Arrive demo

  17. Steering: Pursuit • Intercepting a moving target • Don’t just aim for the target position • Aim for where you *think* the target will be • Look-ahead directly proportional to distance to evader and inversely proportional to speed • Another modification of seek

  18. Steering: Pursuit • Vector2D SteeringBehaviors:: Pursuit(const Vehcile* evader) • Seek (evader->Pos() + evader->Velocity * LookAheadTime) LookAheadTime = ToEvader.Length() / me->MaxSpeed() + evader->Speed()

  19. Steering: Pursuit current evader current Steering force (flee) desired Current + = Desired Pursuit Demo

  20. Steering: Evade • Vector2D SteeringBehaviors:: Evade(const Vehcile* pursuer) • Opposite of pursuit: • Evader flees from the estimated future position.

  21. Steering: Evade • Flee (persuer->Pos() + persuer->Velocity * LookAheadTime) LookAheadTime = ToPersuer.Length() / me->MaxSpeed() + persuer->Speed()

  22. Steering: Wander • Steering force for a ‘random walk’ • Just moving randomly is unconvincing and erratic • No one out on a walk stops and reverses course frequently • Perlin noise – Ken Perlin (Tron) • Remember our earlier statement: • Randomness can make us look smarter!

  23. Steering: Wander • One solution: • Project a circle in front of the agent • Steer towards a target that is constrained to move along the perimeter of this circle • Target adjusts in small increments along the perimeter • Adjust algo based on circle radius, target adjustments, circle distance

  24. Steering: Wander Wander Demo wander radius wander distance

  25. Steering: Obstacle Avoidance • Need concept of a bounding box (or detection box) • Rather than detecting a collision on a complex boundary • Can get incrementally more detailed in collision detection

  26. Steering: Obstacle Avoidance • Collision detection is a detailed endeavor • Only recently distinguished hits by location in FPS

  27. Steering: Obstacle Avoidance Dot product of agent (purple) vector and vector to obstacle will be positive if obs is in front – this obstacle can be ignored. obs Detection box length proportionate to speed

  28. obs Steering: Obstacle Avoidance Extend obstacle boundary by ½ width of detection box. If new boundary crosses agent vector, collision occurs at intersection of new boundary and detection box. obs

  29. obs Steering: Obstacle Avoidance obs

  30. Steering: Obstacle Avoidance • Vector2D SteeringBehaviors::ObstacleAvoidance(const std::vector<BaseGameEntity*> &obstacles) • Once an obstacle that will cause a collision is detected, need a steering force to avoid. • Just like driving: slow down and avoid

  31. Steering: Obstacle Avoidance • Lateral force in proportion to obstacle’s position with respect to the agent • Braking force in proportion to distance to obstacle (force acting along opposite vector of agent) • Code omitted • Obstacle Avoidance Demo

  32. Steering: Hide • Position yourself such that an obstacle is always between you and an agent • Constant (c) for distance from obstacle ‘Arrive’ to this position c

  33. Steering: Hide • Vector2D SteeringBehaviors::Hide(const Vehicle* target, vector<BaseGameEntity*>& obstacles) • For Each Obstacle • Calculate A hiding position for this obs • Calculate Distance to hiding position • Is Hiding Position Available? • Yes – Call ‘Arrive’ to Closest • No – Evade • Hide Demo

  34. Steering: Path Following • Useful for creating bot behaviors such as patrolling • Used for multi-stage navigation • Very commonly used in most games • Some other function determines a goal location – more difficult • Calls FollowPath to get there

  35. Steering: Path Following • Vector2D SteeringBehaviors::followPath( ) • Just ‘seek’ each waypoint in turn • Can ‘arrive’ at final destination • Path object in GameWorld class • Path class omitted • Path Following Demo

  36. Steering: Group Behavior • Agents can react to other agents • Multi-agent aware • From our earlier discussion of environment and agent attributes • Mimic nature • Simple creatures performing complex activities - ACO • Neighborhood – pervasive concept. What is the sphere of influence? • This idea present in many AI concepts

  37. Steering: Flocking • Combination of • Separation • Alignment • Cohesion • Flocking Demo

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