Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto

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Artificial Fishes: Physics, Locomotion, Perception, Behavior. Mar. 30, 2001. Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto. Presentation by Siddharth Dalal. Intro & Background. What do fish do? eat, survive, when compelled by their libidos…. - PowerPoint PPT Presentation

Transcript of Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto

Mar. 30, 2001

Xiaoyuan Tu and Demetri Terzopoulos,

Dept. of CS, University of Toronto

Artificial Fishes: Physics, Locomotion, Perception, Behavior

Presentation by Siddharth Dalal

Intro & Background

• What do fish do?– eat, survive, when compelled by their libidos….

• Physics based graphic modeling

• Worm Dynamics, facial model– more sophisticated spring mass model

• advanced behavioral animation

Any fish is good if caught on the hook.

Overview

Intention focuses sensory data causing behavior

Fishics 1 - Mechanics• Spring Mass Model

wiiii fw

dt

dxq

dt

xdm

2

2

m = massx = positionq = damping factorw = net force due to springsf = external force

Fishics 2 - Hydrodynamics• Swimming - Muscles + Hydrodynamics

Fishics 3 - Motor Controllers• Swim MC• Left and right MC

• Anterior and Posterior of fish - r1, s1, r2, s2• Max params scaled from 0 - 1 to produce varying speeds

Sensory Perception

• Two on board environment sensors:– Vision Sensor - extracts information from scene

geometry, object database, physical simulation. Cyclopean(?) vision - 300o viewing angle.

– Temperature sensor - senses ambient temp. at center of body

Behavio(u)r 1

• Intention based on– Habits– Mental State– Incoming Sensory Information

• decides behavior routine

• incremental - needs memory

Behavior 2 - Habits and Mind

• Habits - does fish like brightness, schooling, male or female (yes this is in habits)

• Mental State– Three mental states - HLF - hunger, libido, fear– H= min[1-n(t)R(Δt)/α, 1]– L=min[s(Δt)(1-H(t)), 1]– F=min[Σf, 1], f=min[D/d(t), 1]

(Fish like sex after dinner )

Intentions 1• Intentions

– avoid,– escape– school– eat– mate– leave– wander

Intentions 2

• Features of Generator– Persistence in intentions - no dithering– focusser - focus on most important intention

• Create ‘abnormal fish’– warp intentions

Intentions 3

• Behavior routines:– eight - avoid static obstacle, avoid fish, eat,

mate, leave, wander, escape, school– chasing target subroutine– other subroutines - looping?, circling,

ascending?, nuzzling

Fish Type = Warped Intentions

• Artificial Fish Types– Predators

• don’t escape, mate or school

• always cruise, so don’t leave

Fish Type = Prey Fish Grey Fish

• Artificial Fish Types– Prey

• school

• evade predators

Pacifists

• Artificial Fish Types– Pacifist

• no school, no escape

• just mate

• complex mating behavior implemented…– fish i chooses partner j

– criteria if i is female/male

– looping, circling, chasing-target, nuzzling

– etc.

Result

• 10 fish, 15 food particles, 5 static obstacles at 4fps on SGI R4400 Indigo2

• Future:– reproduction– other work

Links

• http://www.dgp.toronto.edu/people/tu/tu.html• http://citeseer.nj.nec.com/tu94artificial.html• http://www.cs.toronto.edu/~dt/

Guests and fish start to stink after two days.