Mar. 30, 2001 Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto Artificial Fishes: Physics, Locomotion, Perception, Behavior Presentation.

Slides:



Advertisements
Similar presentations
THE WORM Caenorhabditis elegans as a model organism.
Advertisements

Invertebrates By Alenna Naeve A.N.
On your Graphic Organizer Draw a picture of your favorite animal List 4 characteristics explaining why this animal is your favorite Why Do Animals Behave.
By: Jennifer Richardson
Statistical Analysis of Fluctuating Variables on the Stability of Predator Prey Relationships Lenny Li Computer Systems Lab Period 4.
Robotics applications of vision-based action selection Master Project Matteo de Giacomi.
Group Behaviors and Artificial Life Claire O’Shea COMP 259 – Spring 2005.
Crowd simulation Taku Komura. Animating Crowds We have been going through methods to simulate individual characters We have been going through methods.
Characteristics and Adaptations
Behavioral and Intelligence on Predator Prey Relationships Lenny Li Computer Systems Lab Period 4.
1Notes  Assignment 2 is out  Flocking references  Reynolds, “Flocks, Herds, and Schools…”, SIGGRAPH’87  Tu and Terzopoulos, “Artificial Fishes…”, SIGGRAPH’94.
The Ocean Depths The ocean depths include a number of distinct habitats: – Epipelagic zone - upper 200 meters; the photic zone – Mesopelagic zone – m.
Flocking and Group Behavior Luv Kohli COMP259 March 24, 2003.
 Background  Problem Statement  Solution  Mechanical › Azimuth › Elevation › Concepts › Static and Dynamics of System  Software › SatPC32 › Interpolation.
Animat Vision: Active Vision in Artificial Animals by Demetri Terzopoulos and Tamer F. Rabie.
Fast and Robust Legged Locomotion Sean Bailey Mechanical Engineering Design Division Advisor: Dr. Mark Cutkosky May 12, 2000.
CS274 Spring 01 Lecture 5 Copyright © Mark Meyer Lecture V Higher Level Motion Control CS274: Computer Animation and Simulation.
1cs426-winter-2008 Notes  Please read: C. Reynolds “Flocks, Herds, and Schools…” SIGGRAPH ‘87
Crowd Simulation Seminar ”Steering Behaviors For Autonomous Characters” By Craig W. Reynolds Rudi Bonfiglioli ( )
Bottlenose Dolphins are Amazing By Sarah Winter. Scientific Clarifications Classification: Kingdom Animalia Phylum Chordata Class Mammalia Order Cetacea.
05/09/02(c) 2002 University of Wisconsin Last Time Global illumination algorithms Grades so far.
By: JA. Introduction The scientific name for Great White Shark is Carcharodon Carchias. Is it endangered? Yes it is. They live for about years but.
Fuzzy control of a mobile robot Implementation using a MATLAB-based rapid prototyping system.
Class Osteichthyes aka: Bony Fishes.
The Nervous System Chapter 49
Artificial Intelligence Intro Agents
Game Engine Programming. Game Engine Game Engine Rendering Engine (OGRE) Rendering Engine (OGRE) Physics Engine (Bullet) Physics Engine (Bullet) Input/Output.
Animals use and sense energy to: avoid predators find food and find mates. n.edu/chudler/amaze.ht ml
Natural Selection Problem
Interactions in Nature Mandek Richardson STARS Program University of South Florida.
Computer Graphics 2 In the name of God. Outline Introduction Animation The most important senior groups Animation techniques Summary Walking, running,…examples.
Platypus Floppy, Swimming Platypus By: AC.
Artificial Intelligence Intro Agents
제 6 주. 응용 -2: Graphics Artificial Life for Computer Graphics D. Terzopoulos, Communications of the ACM, vol. 42, no. 8, pp. 33~42, 1999 학습목표 Understanding.
Copyright Pearson Prentice Hall
Introduction to Animals Chapter 26. General Features of Animals Animals are multicellular, heterotrophic organisms with cells that lack cell walls. Multicellular.
Whales and Their Adaptations
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
CLASS 10 SCENE GRAPHS BASIC ANIMATION CS770/870. A scene Graph A data structure to hold components of a scene Usually a Tree of a Directed Acyclic Graph.
Autonomous Virtual Humans Tyler Streeter. Contents Introduction Introduction Implementation Implementation –3D Graphics –Simulated Physics –Neural Networks.
Natural Selection Problem
Animating Idle Gaze Humanoid Agents in Social Game Environments Angelo Cafaro Raffaele Gaito
Your tables are your teams. Place each question under the correct learning target. Work together to get the most points. This is a fun way to study for.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Modelling Fish Behaviour Advisor : Dr. Hsu Presenter :
AG-WL-4 WILDLIFE CHARACTERISTICS & BEHAVIOR. ANIMAL ADAPTION Over time, animals will adapt to their changing habitat or they will become extinct Adaptation.
Adaptation Any characteristic (structure or behavior) that helps a plant or animal survive.
Group Behaviors. Seminal flocking papers Craig Reynolds Flocks, Herds, and Schools: A Distributed Behavioral Model – SIGGRAPH 1987Flocks, Herds, and Schools:
Animation Animation is about bringing things to life Technically: –Generate a sequence of images that, when played one after the other, make things move.
Scales of Ecological Organization Organism Population Community Ecosystem Biosphere.
Robot Intelligence Technology Lab. 10. Complex Hardware Morphologies: Walking Machines Presented by In-Won Park
Animal Behavior Innate and Learned Behaviors. Behavior An activity or action that helps an organism survive in its environment. Behavior can be thought.
Animal Kingdom Ch 25 What is an Animal?. Important Animal Facts Animal Kingdom can be split up into main groups, vertebrates (with a backbone) and invertebrates.
Autonomous Dynamically Simulated Creatures for Virtual Environments Paul Urban Supervisor: Prof. Shaun Bangay Honours Project 2001.
Simulation of Characters in Entertainment Virtual Reality.
Artificial Fishes: Physics, Locomotion, Perception, Behavior
Animating Human Locomotion
Computer Animation Algorithms and Techniques
Animal Behavior Notes.
All about Evolutionary Psychology and its functions
Angelo Loula, Ricardo Gudwin, Charbel Nino El-Hani, and Joao Queiroz
How Do Animals Adapt? Animals inherit characteristics from their parents. These special features and behaviors help them survive.
Survival in an Ecosystem
Swarm simulation using anti-Newtonian forces
Life-sustaining processes and survival of species
How Do Animals Adapt? Animals inherit characteristics from their parents. These special features and behaviors help them survive.
Flocking and Group Behavior
Mechanisms of evolution
Mechanisms of Evolution
Why object remain stationary
Presentation transcript:

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 m = mass x = position q = damping factor w = net force due to springs f = 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 to produce varying speeds

Sensory Perception Two on board environment sensors: –Vision Sensor - extracts information from scene geometry, object database, physical simulation. Cyclopean(?) vision o 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

Guests and fish start to stink after two days.