1 Seminar Crowd Simulation Introduction. 2 Who am I? Roland Geraerts Assistant professor Robotics...
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Transcript of 1 Seminar Crowd Simulation Introduction. 2 Who am I? Roland Geraerts Assistant professor Robotics...
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Seminar Crowd Simulation
Introduction
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Who am I?
Roland Geraerts Assistant professor Robotics background Research on path planning and
crowd simulation
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Who are you?
Master GMTE? Course Game Design? Course Motion and Manipulation? Interest in Games? Why do you follow the seminar? Interest in thesis projects? Who has exciting hobbies?
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Goal of the seminar
To obtain knowledge of current research in path planning and crowd simulation Study and discuss papers
To understand the limitations of the current techniques Determine the limitations and open problems in the papers
To become a very critical reader Hand in many assessments of papers
To understand the state-of-the-art in current games and how this could be improved Study path planning in existing games Write paper about the applicability of new techniques
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Why this seminar
Path planning and crowd simulation are important research topics in Utrecht Mark Overmars, Roland Geraerts, Frank van der Stappen,
PhD students (Ioannis Karamouzas, Saskia Groenewegen) Relation to animation research
Gate project 19 million Euro Dutch project
on game technology and applications
Thesis projects Future PhD positions
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Practical aspects
Meetings Tuesday 13.15-15.00 BBL-069 Friday 15.15-17.00 BBL-071
Presence is mandatory If you cannot come for a good reason
• Let me know beforehand• Hand in abstracts before meeting
Website http://www.cs.uu.nl/docs/vakken/mcrs/ Check regularly for announcements and changes Download papers Find the secret page
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Assignments
Present two papers Each 30 minutes plus 15 minutes discussion
Write paper abstracts/assessments Read papers before the presentation One page per paper
• Abstract in your own words• Critical assessment
– Main limitations and open problems– Surprising and innovative elements– Do the authors claim too much, make many assumptions, draw
conclusions that are too general, not correctly setup their experiments?
• Two-three questions or points for discussion Hand in the two pages (on paper) on the day of the
presentation• Use headings: Summary, Assessment, Questions
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Assignments
Study path planning in a modern game Investigate what goes wrong (path planning, crowds) Make a video (.wmv to make sure it works) Make 3 slides Bring them with you next Tuesday (May 3) for discussion
Paper on path planning/crowd simulation in games At the end of the seminar (July 1) Write a paper (10 pages) on how the new techniques can be
used in games Based on the problems in two example videos
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Grading
Game study 5% Presentations 15% + 25% Abstracts 20% Paper 25% Active participation 10%
To qualify for second change exam The original mark should at least be a 4; Actively participate in at least 75% of the meetings; Give both presentations satisfactory.
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Tentative scheduleWeek Date Topic Speaker Deadline
17 April 26 Introduction Roland Paper 0
April 29 Overview path planning research Roland Abstracts
18 May 3 Current problems in games Students Assignment 1
May 6 No seminar
19 May 10 Path planning Students Abstracts
May 13 Path planning Students Abstracts
20 May 17 Social force models Students Abstracts
May 20 Social force models Students Abstracts
21 May 24 Social force models Students Abstracts
May 27 Flow Students Abstracts
22 May 31 No seminar
June 3 No seminar
23 June 7 Flow Students Abstracts
June 10 Crowds Students Abstracts
24 June 14 Crowds Students Abstracts
June 17 Behavior Students Abstracts
25 June 21 Massive crowds Students Abstracts
June 24 No seminar?
26 June 28 Crowd evaluation Students Abstracts
July 1 Rendering/GPU techniques Students Assignment 2
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Path planning
Goal: bring characters (or a camera) from A to B Also vehicles, animals, camera, …
Requirement: fast and flexible Real-time planning for thousands of characters Individuals and groups Dealing with local hazards Different types of environments
Requirement: visually convincing paths The way humans move Smooth Short Keep some distance (clearance) to obstacles Avoid other characters …
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Do we need a new path planning algorithm?
Robotics Games
Nr. entities a few robots many characters
Nr. DOFs many DOFs a few DOFs
CPU time much time available little time available
Interaction anti-social social
Type path nice path visually convincing path
Environment 2D (or terrain), 3D 2D, 2.5D (e.g. bridges)
Algorithms can be simple must be simple
Correctness fool-proof may be incorrect
typical differences
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Path planning algorithms in games
Networks of waypoints Scripting Grid-based A* Algorithms Navigation meshes Local approaches Flocking Cheating
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Errors in path planning
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Errors in path planning
Networks of waypoints are incorrect Hand designed Do not adapt to changes in the environment Do not adapt to the type of character
Local methods fail to find a route Keep stuck behind objects Lead to repeated motion
Groups split up Not planned as a coherent entity
Paths are unnatural Not smooth Stay too close to network/obstacles
Methodology is not general enough to handle all problems
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What we study in the seminar
Methodology/framework that solved these problems Developed in Utrecht (still in development) Applications (characters, cameras, groups, crowds, …)
Local character behavior How do people walk toward locations How do they avoid each other Social force models
Crowd behavior Flow models Planning approaches Crowd evaluation Massive crowds Crowd rendering
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The Explicit Corridor Map: Full/generic representation free space
The Explicit Corridor Map Navigation mesh, or: a system of collision-free corridors Data structure: Medial axis + closest points Computed efficiently by using the GPU
Explicit Corridor Map (2D) Explicit Corridor Map (multi-layered)
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The Explicit Corridor Map:Experiments
Footprint and Explicit Corridor Map: 0.3sCity environment
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Corridors (macro scale)
Computing a corridor: provides a global route Connect the start and goal to the Medial axis Find corresponding shortest path in graph Corridor: concatenation of cells of the ECM
Corridor A corridor with small obstacles
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The Indicative Route Method (meso scale):Introducing flexibility
A path planning algorithm should NOT compute a path A one-dimensional path limits the character’s freedom Humans don’t do that either
It should produce An Indicative/Preferred Route
• Guides character to goal A corridor
• Provides a global (homotopic) route
• Allows for flexibility
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“Algorithm” Compute a collision free indicative route from A to B Compute a corridor containing the route Move an attraction point along the indicative route
• The attraction point attracts the character • The boundary of the corridor pushes it away• Other characters and local hazards push the character away
The Indicative Route Method (meso scale):Introducing flexibility
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Local method (micro scale)
Boundary force Find closest point on corridor boundary Perpendicular to boundary Increases to infinity when closer to boundary Force is 0 when clearance is large enough (or when on the MA)
• Depends on the maximal speed of the character• Should be chosen such as to avoid oscillations
Steering force Towards attraction point Can be constant
Obtain path Force leads to an acceleration term Integration over time,
update velocity/position/attraction point Yields a smooth (C1-continuous) path
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IRM method
Resulting vector field Indicative Route is short path
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IRM method:Experiments
City environment Corridor and path: 2.8ms
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Crowd simulation
Method can plan paths for a large number of characters Force model is used for local avoidance Path variation models are integrated,
adding more realism Additional models can be
incorporated easily Goal oriented behavior
Each character has its own long term goal
When a character reaches its goal, a new goal is chosen
Wandering behavior Attraction points do a random walk on the underlying graph
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Collision-avoidance model
Particle-based approaches E.g. Helbing model When characters get close to each other they push each other
away Force depends on the distance between their personal spaces
and whether they can see each other Disadvantages
Reaction is late Also reaction when no collision Artifacts
Goal forceAvoidance forceResulting force
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Improved collision-avoidance model
Collision-predication approach When characters are on collision course we compute the
positions at impact (of personal spaces) Direction depends on their relative position at impact Force depends on the distance to impact Care must be taken when combining forces
Goal forceAvoidance forceResulting force
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Improved collision-avoidance model
Advantages Characters react earlier (like in real life) Characters choose routes that deviate only marginally from
original route (energy efficient) Emergent behavior, e.g. lane formation and characters
grouping Fast (thousands of characters in real time)
Helbing Collision prediction
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Improved collision-avoidance model
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Improved collision-avoidance model
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Current work
Also allow speed changes Deal with small groups
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Further work
Get different types of high-level crowd behavior Wandering Shopping Hanging around …
Combine different types of moving entities People Bikes Cars Animals
Path planning in 3D
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First assignment
Study path planning/crowd simulation in a modern game Pick a game in which there is a lot of motion
• Dynamic changes in the environment• Computer controlled characters (enemies, buddies, …)• Groups of characters (e.g. in RTS games)• Crowds (e.g. GTA, Assassin’s Creed, Sim games)
Investigate what goes wrong• Deliberately try to create problems
– Destroy objects/buildings– Stand in the way of moving characters– Park a car on the sidewalks
• Look at – Quality of motion– Occurrence of collisions– Repeated motions (lack of variation), …
Bonus points for spotting errors in 2.5D/3D games, dynamic situations
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First assignment
Study path planning/crowd simulation in a modern game Make a video (preferably a .wmv file)
• Fraps• Use a camera or webcam• Sometimes in-game possible
Make (at least) three slides in PowerPoint• Name of the game, your name, picture, type of game• Video(s)• Description of the main things that go wrong and why (according to
you) Take with you on USB stick next Tuesday!
• Explain and discuss (5 - 7.5 minutes)
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Some results of last year’s assignment