more complex movement ai
DESCRIPTION
More Complex Movement AI. Flocking and more. Movement in Groups. NPC groups can move in cohesive groups not just independently Meadow of sheep grazing? Hunting flock of birds? Ants? Bees? Creatures? Other types of computer controlled NPCs Humans, Orcs , Catapults? Squadrons of aircraft? - PowerPoint PPT PresentationTRANSCRIPT
More Complex Movement AI
Flocking and more
NPC groups can move in cohesive groups not just independently◦ Meadow of sheep grazing?◦ Hunting flock of birds?◦ Ants? Bees? Creatures?
Other types of computer controlled NPCs◦ Humans, Orcs, Catapults?◦ Squadrons of aircraft?◦ Friendly soldier squads?◦ Simulate crowds of people loitering?
Movement in Groups
Coordinated group movement, the idea:◦ To have the NPCs move with the illusion of having
purpose and coordination One of the earliest, most successful group
behavior Flocking◦ “Flocks, Herds, and Schools: A Distributed
Behavioral Model”, Craig Reynolds, SIGGRAPH 1987
◦ Originally intended for birds, fish and other creatures, but it can be modified for other types of NPCs
Movement in Groups
Flocking
Term used by Craig Reynolds to refer to this simulated flocks
Leaderless flock – able to stick in a group 3 simple rules
◦ Cohesion◦ Alignment◦ Separation
Neighborhood: Defines the area where these rules will come to effect
Boids
Have each unit steer towards the average position of its neighbors
Units are attracted to one another as long as they are within range
Cohesion
Have each unit steer so as to align itself to the average heading of its neighbors.
Match direction of units around it that it can detect
Alignment
Have each unit steer to avoid hitting its neighbors.
Units are repelled by non-member units or obstacles. Repel effect is inversely prop. to distance from unit
Separation
Flocking neighborhood creates a range that units can detect for other same-group units, other-group units
Neighborhood
Some implementations use two neighborhoods – one for detection of units, one for separation to avoid other units
Neighborhood
Typically, a visibility arc or field-of-view (FOV) is used to define the neighborhood
Is this practical? To what extend is each unit aware of its
neighbors?
Neighborhood
Each unit is aware of its local surroundings Each unit does not necessarily know what
the entire group is doing at any given time
Unit Visibility
Visibility arc defined by 2 parameters – arc radius r and angle θ
How do these parameters affect flocking motion?
Unit Visibility
Large radius? Small radius? Wide FOV? Narrow FOV?
Unit Visibility
Narrow FOV: Squadron of jets, Sneaking up behavior
Wide FOV: Group of birds, Military army
FOV determines formation
Steering forces to be applied on the units Treat each unit as a rigid body that is able
to turn and apply net steering force accumulated from each flocking rule
2 important techniques when implementing flocking◦Tuning is required so that no single rule
dominates◦Modulation of steering forces so that
contribution is not constant for all units
Steering for Flocking
In each game loop◦ Cycle thru all units in the flock to acquire data
(direction, speed, etc.) from unit’s neighbors◦ For each unit, update with net steering force from
the three rules Each unit must update its view of the world
each game loop (cycle thru all units in the flock)
Refer to textbook for more details on the implementation code snippet
Implementation
void DoUnitAI(int i) { int j; int N; // Number of neighbors
Vector Pave; // Average position vector Vector Vave; // Average velocity vector Vector Fs; // Net steering force Vector Pfs; // Point of application of Fs Vector d, u, v, w; double m; // multiplier, +1 or -1bool InView; bool DoFlock = WideView||LimitedView||NarrowView; int RadiusFactor; . . .
}
Sample Variable Set
Calculate average position – vector sum of their respective positions divided by total number of neighbors
Determine direction to turn and angle to steer towards
Steering force effected = Direction multiplier * Max steering force * angle of steering / scale factor
Cohesion - Implementation
Normalize each unit’s velocity vector to get heading unit vectors
Calculate average heading of all units – sum of heading unit vectors divided by total number of neighbors
Effected steering force is calculated same way as cohesion
Alignment - Implementation
Separation is enforced by steering away from any neighbor that is within view AND within prescribed minimum separation distance
Because this steering force is corrective, direction multiplier goes the opposite way
Effected steering force= Direction multiplier * Max steering force * (Unit length * separation factor) / separation distance
Separation - Implementation
Flocking would be much more realistic if units also avoid running into objects in the game world
To detect whether an obstacle is in the unit’s path ahead, imagine that each unit has “feelers” like those on insects!
Well, if one feeler is not enough, maybe you might need a few feelers?
Let’s see how a single “feeler” works…
Obstacle Avoidance
v : “feeler” Calculate vector a Project a onto v by dot product
to obtain p Subtract p from a to get vector b Test conditions:
1. Magnitude (p) < Magnitude (v)2. Magnitude(b) < Radius (r)
If both tests pass, corrective steering required, otherwise unit can continue on its current heading
Obstacle Avoidance
Corrective force can be calculated as inversely prop. to distance from unit to the center of obstacle or Magnitude (a)
Effected steering force= Direction multiplier * Max steering force * (Collision Visibility Factor * Unit length for Magnitude(v) / Magnitude(a) )
Obstacle Avoidance
This obstacle avoidance algo will not necessarily guarantee zero collisions between units and obstacles. What are some likely problems?
What we have seen so far only applies to circular obstacles. What about block (rectangular) obstacles or other free forms shapes?
Obstacle Avoidance - Remarks
So far, flocking behaviors are leaderless By combining classic flocking with leader-
based AI, many new possibilities are available!
Flocks may have greater purpose if follow a leader
Question: How to designate leader? Should we “appoint” a unit as leader? Or should we let them sort out themselves who should be a leader?
Follow the Leader
Let’s focus on this particular method Advantage: Any unit can become a leader
at any given time, flock will not be leaderless if leader gets destroyed or separated from flock
Once a leader is established, we can implement any number of rules to have the leader do something meaningful ◦ Execute pattern movement or patrolling◦ Chase or evade or intercept something
Let them sort themselves!
Can you figure out an algorithm to do this?
Leader Check
A possible solution:◦ Determine the number of units directly in front of
or within view of current unit being processed (velocity directions are available for use)
◦ If no other units are directly in front of the unit, it becomes the leader. The rest follows flocking rules
Any more ideas?
Leader Check
Follow the Leader AI adds an interesting dimension into flocking and group coordinated behavior
More than one leader (of different purposes) can also be implemented
You can also implement flocking behavior for player-friendly/assisting NPCs where the “leader” is simply the player
Follow the Leader