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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