1. CIS 488/588 Bruce R. Maxim UM-Dearborn
Steering Behavior
CIS 488/588
Bruce R. Maxim
UM-Dearborn
12/5/2018

2. Alife
Focuses on behavior of individual creatures that combine into complex pattern
The interaction of simple patterns can create incredibly sophisticated simulations
Alife can be used to simulate crowds in complex environments using streering behaviors
This was the basis of Conway’s game of life
12/5/2018

3. Assumptions
Steering behaviors assume the existence of a lower-level of the engine to handle locomotion
The locomotion system processes each characters position and velocity
When player stops pressing a key movement stops (heavy duty friction)
In many games a key press is likely to control velocity and not acceleration
In this chapter velocity persists and the AI applies an acceleration to effect steering
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4. Seeking and Fleeing Seeking Fleeing Pursuit Evasion
Steering behavior moving creatures toward target
Fleeing
Steering behavior moving creatures away from target
Pursuit
Seeking a moving target
Evasion
Avoiding a moving target
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5. Enhancements - 1 Wandering Projecting Targets
Make random behavior appear a little more purposeful
Patterns should be slightly unpredictable, but not arbitrarily random
Could accumulate steering values and filter them using the sin function (gives both + and – values)
Projecting Targets
Look ahead randomly for point where target is predicted to be and move there
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6. Seeking Implementation
#compute velocity vector toward target
desired_velocity = truncate(position_target,max_speed);
#compute steering force
steering_force = desired_velocity velocity;
Arrival behavior can be simulated by slowing down the velocity to something less than max_speed within some distance from target
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7. Fleeing Implementation
// compute velocity vector toward target
desired_velocity = truncate(position_target,max_speed);
// compute steering force
steering_force = - desired_velocity velocity;
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8. Modeling Flocks
There are additional steering components needed (e.g. alignment and cohesion) when modeling group behaviors
These require position and orientation information for neighbors rather than surrounding obstacles
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9. Generalized Obstacle Avoidance
function avoid_obstacles {
project future position based on velocity
if collision predicted
project empty position away from collision
compute turn to get to empty position }
if obstacle is within critical distance
determine braking force // slow or stop
apply steering and braking forces
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10. Comments
Application of steering and braking forces is easy since our interface outputs turn and move values
The challenge is in acquiring the inputs used to determine future collisions
This algorithm requires environment knowledge, intersection tests, collision normal forces, and location of nearby empty spaces
This makes it unsuitable for implementation
12/5/2018

11. Updated Obstacle Avoidance - 1
function avoid_obstacles2 {
check sensors for free space front, left, right
if front collision predicted
find furthest obstacle on right or left
determine best side to turn toward
compute turn to seek that free space }
if front obstacle is within critical distance
determine braking force // slow or stop
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12. Updated Obstacle Avoidance - 2
if obstacle on left
adjust steering to step right
if obstacle on right
adjust steering to step left
apply steering and braking forces }
Implementing this involves simple translation to C++ and finding suitable parameters during experimentation phase
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13. Enhancements - 2 Forced Exploration
Keep track of previous positions a flee from them
Done using single vector (provenance)pointing toward last position
Coefficients a + b = 1
provenance = a * previous +
b * provenance
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14. Advantages - 1 Simplicity Reliability Predictability Efficiency
Hard to find a simpler architecture
Reliability
Most situations can be identified requirements
Predictability
No ambiguity, each rule written explicitly
Efficiency
Very low computational overhead
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15. Disadvantages - 1 Local traps Testing
Can still get stuck in corners, if it begins to turn one way and then decides the another way might be better
Testing
This approach requires extensive testing because there are so many parameters to play with
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16. Disadvantages - 2 Realism Scalability
Robotic movement is not as smooth as it might be (local decisions are not integrated into a plan)
Scalability
Additional behaviors are added by adding additional lines of code and recompiling
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17. Marvin
Uses obstacle sensors and steering behaviors to prevent collisions in a reactive fashion
Uses chapter enhancements to improve its wandering behavior
12/5/2018