12 - More Steering. from Milligan, "Artificial Intelligence for Games", Morgan Kaufman, 2006

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1 12 - More Steering from Milligan, "Artificial Intelligence for Games", Morgan Kaufman, 2006

2 Separation commonly used in crowds, when all characters moving in roughly the same direction (repulsion steering) not good for crossing paths, use collision avoidance for that mostly has zero output, unless another character closer than some threshold distances similar to evade, but strength is related to distance inverse linear law strength = maxacceleration * (threshold - distance) / threshold inverse square law strength = min ( k * distance * distance, maxacceleration ) for multiple other characters, calculate each steering output and sum O(n) for simple list of characters O(log n) for a tree-based spatial index used in combination with other behaviors

3 Collision Avoidance with lots of characters in the same space with crossing trajectories, need constant collision avoidance a simple approach : use a variation of Evade or Separation, but only engage if target is within a code in front of the character if multiple characters in cone: find their average and evade that OR, find the closest and evade it simple approach doesn't consider if collision will occur, just evades anything nearby simple approach also misses when two characters are mutually out-of-cone until the moment of impact ignored character to avoid half-angle of the cone in-cone, but no collision out -of-cone, but colliding

4 Collision Avoidance a better approach: check if characters will collide at current velocities find point of closest approach test if distance to closest approach within threshold closest approach not necessarily where paths cross, as velocities may differ t_closest = time of closest approach = (dp * dv) / dv 2 dp = relative position = target.position - character.position dv = relative velocity = target.velocity - character.velocity if t_closest < 0: already moving away, no action needed else: find position at time of closest approach: p_char_closest = character.position + character.velocity * t_closest p_target_closest = target.position + target.velocity * t_closest use these to drive Evade for groups, best to use for closest target only

5 Obstacle and Wall Avoidance Can use collision avoidance to avoid obstacles by assuming they are spherical stay outside of sphere's radius okay for small objects, no good for walls. Better: Cast one or more finite-length rays from character, find intersection with wall Use the intersection point and wall normal to compute a target location at a fixed distance from the surface, and Seek it. target

6 Collision detection problems single ray-cast usually not good, triple rays better configurations: parallel side rays central ray with short whiskers whiskers only single only

7 The Corner Trap can occur near corners of objects, esp. if acute angle first one ray detects collision, turns character away then the other ray is colliding, turning character the other way result: character homes-in on corner and gets stuck there possible solution: use a wider fan of collision rays and adaptive fan angles. If moving successfully without collision, narrow the fan angle (move through tight passages) If collide, widen the fan angle. If many collisions, widening fan will tend to avoid corner trap. best but expensive solution: test for collisions with the volume swept out by character on predicted path compromise that mostly works: use adaptive fan angles with one long ray and two shorter whiskers

8 Family Tree of Steering Behaviors Align LookWhereYoureGoing Face Wander Seek/ Flee Pursue/Evade Path Following Obscacle Avoidance VelocityMatch Arrive ForceField Separation

9 Combining Steering Behaviors Higher level tools like pathfinding can select final goal. With more than one steering behavior active at once, we can simultaneously: reach goal avoid collisions with other characters tend towards safety avoid bumping into walls grab powerups etc

10 Blending and Arbitration keep a portfolio of steering behaviors execute them all blending: combine with weights or priorities arbitration: select one or more steering behaviors to have complete control in practice, combine both

11 Weighted Blending simple ex) rioting crowd: blend of "Arrive at center of mob" and "maintain Separation from nearby rioters" assign a weight to each behavior evaluate each in isolation compute weighted linear sum of results clamp to maximum acceleration. sum = (0,0) for i in behaviors: sum := weight[i] * steeringoutput[i] if length(sum) > maxacceleration: clamp picking weights is tricky guesswork, or can use neural nets, genetic algorithms, etc can create "flocking" behavior, as in Craig Reynold's "boids" (see Millington's example program): separation - move away from boids that are too close alignment and velocity matching - match direction and velocity of neighbors cohesion - move toward the center of the flock

12 Problems with blended steering getting stuck - esp in cluttered environments equilibrium - two behaviors cancel each other out: no motion (trapped at an equilibrium point) unstable equilibrium - small errors accumulate to break equilibrium stable equilibrium - a "basin of attraction" pulls character into equilibrium with no escape enemy Flee enemy Flee enemy Seek target Seek target enemy target Flee enemy target basin of attraction enemy

13 Problems with blended steering constrained environments blending works best in open environments 3d motion outdoors has fewest contraints indoors, cluttered is problematic can't simultaneously avoid obstacle and chase can miss narrow doorways resulting acceleration collision ray route of character result wall Pursue Pursued enemy wall avoidance Pursued enemy

14 Problems with blended steering nearsightedness "greedy" nature of steering algorithms no way to augment steering, will always miss problems beyond its "horizon" best to integrate pathfinding into steering collision ray route of character Pursued enemy

15 Priorities Some behaviors don't always produce an acceleration (Arrive, CollisionAvoidance, Separation) Some always do (Seek, Evade) When blended, some can dilute the effects of others. Consider blending Seek and CollisionAvoidance: Seek is always at maximum acceleration if blended with CollisionAvoidance, the avoidance behavior is never more than 50% of total may not be enough to escape trouble

16 Priorities Replace weights with priorities: divide steering behaviors into groups of blended steering behaviors assign a priority to each group consider each group in priority order if group's output less than a threshold, skip it, and consider next highest priority group when a group with output above threshold is found, use it as steering behavior.

17 Priorities Ex) Groups in prority order: collision and wall avoidance group, separation group, pursuit group when near a collision, collision group's output is strong and is used, taking over to avoid collision. when no collisions, try to keep separate from neighbors if in the clear, no collision or separation output, use Pursue output Can add a Wander as lowest priority group to avoid some equilibria, but can still be caught in large basins