Crowd Simulation Seminar

1. ”Steering Behaviors For Autonomous Characters” By Craig W. ReynoldsRudi Bonfiglioli (3565025) Crowd Simulation Seminar

2. Outline • About the author and the paper • Introduction, previous work, general concepts • Main part: • Locomotive model • The steering layer – Behaviors • Combining Behaviors • Experiments • Assessment

3. Who? • Craig W. Reynolds, born in 1953 • Creates ”Boids” in 1986: artifical life program that simulates the flocking behavior of birds • Interested the field since then, mainly working in Sony R&D dep. In the US • Worked also on the films ”Tron” ('86) and ”Batman Returns” ('92)!

4. What? • Paper discussed at the GDC 1999 (656 citations) • Early days: Matrix was not even a movie! • Among first attempts at formalizing a crowd simulation approach: details about choices of words and overlapping related fields

5. Introduction • Focus on autonomous characters meant as situated, embodied, reactivevirtual agents • Situated: share world with similar entities • Embodied: have a physical manifestation • Reactive: have stimuli-driven instincts • Virtual: not just simulation of a mechanical device (easy to describe) but real agents in virtual world

6. Introduction (2) • Behavior: ”improvisation and life-like actions of an autonomous character” • Classical AI instead defines steps to solve problems • Complex: can be divided in layers • We will focus on the middle one

7. Previous Related Work • Robotics: Arkin R. (1987) “Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior” • Perception → Action Mappings expressed in terms of potential fields (not procedural approach) • AI: Costa, M. Feijó, B., Schwabe, D. (1990) ”Reactive Agents in Behavioral Animation” • Artificial Life: Tu, X. Terzopoulos, D. (1994) “Artificial Fishes: Physics, Locomotion, Perception, Behavior”

8. General Concepts • Our ”pipeline”: Signals→(Loco)Motion→Animation • 3 Independent levels? Theoretically, yes • In practice: Signals have to compensate the lower agility of locomotion! Animation model must be able to adapt to different locomotion scenarios! • The paper will try to treat the locomotion level as completely separated from steering level

9. Locomotion Model • Very simple • Not powerful, but general and easy to extend • A steering force (vector) is applied to move it, then Euler Integration • Orientation stores a description of both global and local (different viewpoint) space • No explicit rotations used to update state!

10. Locomotion Model (2) • While moving we mainly have to deal with updating the UP and SIDE vectors • Basic: UP is perpendicular to forward (velocity) direction, SIDE is perpendicular to new UP Vehicle moving on surfaces → easy. UP vector is always aligned with the normal of the surface Vehicle flying → Tricky. Banking: align the local floor (hence also UP) with the apparent gravity due to centrifugal force during a turn

11. Intermezzo • We defined the scope of our problem • We have a locomotion model • Let's move to the above layer: steering • Formal description of many steering behaviors through geometric calculation of desired steer force

12. Behaviors – Seek and Flee • Seek: adjust velocity so that its velocity is radially aligned towards the target Character will eventually pass through target, then turn back • Flee: Similar to seek but the velocity points in the opposite direction

13. Behaviors – Pursuit and Evasion • Pursuit: target is another moving char Try to predict the future position of char, then seek the predicted pos Position T units of time in the future → scale char velocity by T, then add to current pos • Defining T is the key • Evasion: instead, flee from predicted position • Optimal techniques for both pursuit and evasion exist!

14. Behaviors – Offset pursuit • Offset pursuit: steering a path that passes near a moving target without never really touching it • Dynamically compute a target point which is offset by a radius R from the predicted pos, then use seek

15. Behaviors - Arrival • Arrival: like seek, but when close to the target, incremental slow down so that we stop at target position • Max velocity kept until we are inside a circle with radius R (predefined) centered in the target position • Then, velocity is decreased (linearly?)

16. Behaviors – Obstacle avoidance • Obstacle avoidance: both obstacles and character are approximated with spheres • Cilinder projected in the forward direction: If any obstacles intersect it, we just move in the side direction with respect to the center of the nearest obstacle

17. More Behaviors... • Wander, path/wall following, containment • More elaborated behaviors use the simpler ones

18. … Even More Behaviors... • Collision avoidance (unaligned): one/both of the two characters must slow down/accelearate • But which one? • Flow following: powerful way to define the steering behavior to be adopted in an area

19. Group Behaviors • Separation,Cohesion, Alignment: by combining just these 3 group behaviors we can simulate flocking

20. Combining Behaviors • Steering behaviors described since now serve as building blocks for more complex patterns • Sequential switching and Combining • Check whether two behaviors can be combined • How to combine? Blending → calculate both forces • Computationally expensive! → Maybe alternating for some sequential frames? Momentum will be a filter • Defining priorities

21. Wrap Up • The paper defined a new framework for (re)thinking crowd steering • It described both a locomotive model and a way to model the steering layer • The approach is based on the implementation of (averagely) small behavioral patterns: rather then calculating forces depending on a number of rules and constraints (force fields), we compute them in a sequential way depending on the current pattern (state) or by combining more than one

22. Experiments? • Wait, no experiments/application? • Not completely true: opensteer, an opensource framework started by Reynoldshttp://opensteer.sourceforge.net/ • Opensteer is now way more advanced, but fundamental principles (and some routines) are still the same

23. [Show the video] Experiments (2)

24. Assessment • I think the method presented has some nice advantages: + Easy to modify/tune our situation in order to make something happen • Potential fields define a set of rules, hard to tune! + Easy to interact with the other ”layers” because we are always quite ”in control”

25. Assessment (2) • … But there are also a number of drawbacks: - Implementing so many patterns can be long - The implementations of many patterns seem to be quite inefficient (linear in the number of agents, a lot of arithmetic) • Maybe we can apply some space-partitioning/LOD? - Organizing the patterns is not trivial - We will always be limited by the number of patterns and their combination: are they enough?

26. Assessment (3) • Many challenges arise! • What behavioral patterns are the fundamental ones in crowd simulation? • What's the best way to combine them in order to obtain complex behavior? • Can we make crowd phenomena emerge by just defining a small number of patterns and combining them in some way? • ”Big Fast Crowds on PS3” (2006) by Reynolds (PSCrowd) • ”Continuum Crowds” (2006) by A. Treuille, S. Cooper, Z. Popović

27. Questions?