Seminar Crowd Simulation

1. Seminar Crowd Simulation Introduction

2. Who am I? • Roland Geraerts • Assistant professor • Robotics background • Research on path planning andcrowd simulation

3. 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?

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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.

10. Tentative schedule

11. 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 • …

12. Do we need a new path planning algorithm? typical differences 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 pathnice 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

13. Path planning algorithms in games • Networks of waypoints • Scripting • Grid-based A* Algorithms • Navigation meshes • Local approaches • Flocking • Cheating

14. Errors in path planning

15. 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

16. 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

17. 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)

18. The Explicit Corridor Map:Experiments Footprint and Explicit Corridor Map: 0.3s City environment

19. 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

20. 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

21. The Indicative Route Method (meso scale):Introducing flexibility • “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

22. 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

23. IRM method • Resulting vector field • Indicative Route is short path

24. IRM method:Experiments City environment Corridor and path: 2.8ms

25. 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

26. 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 force Avoidance force Resulting force

27. 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 force Avoidance force Resulting force

28. 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

29. Improved collision-avoidance model

30. Improved collision-avoidance model

31. Current work • Also allow speed changes • Deal with small groups

32. 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

33. 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

34. 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)

35. Some results of last year’s assignment