Path Planning

1. Path Planning Introduction

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

3. Who are you? • Master GMTE? • Course Motion and Manipulation? • Interest in games? • Why do you follow this course? • Interest in thesis projects? • Who has exciting hobbies?

4. Learning goals of the course • To become an expert in path planning and crowd simulation • Study and discuss many papers • To understand the limitations of current techniques • Determine the limitations and open problems in the papers • To become a very critical reader • Hand in many assessments of papers • Actively participate in discussions • To understand the state-of-the-art in current games and how this could be improved • Study path planning and crowd simulation in existing games • Write paper about the applicability of new techniques (*)

5. Learning goals of the course • To understand crowd simulation frameworks • Follow the workshop • Integrate some code into a framework (*) • To improve further upon your scientific skills • Participate in discussions and lead them • Give better presentations • Know how to set up experiments better (workshop) • Write better review reports and assessments

6. Why this course • Path planning and crowd simulation are important research topics in Utrecht • Roland Geraerts, Frank van der Stappen, Wouter van Toll, Norman Jaklin, Arne Hillebrand, Sybren Stüvel • Relation to animation research (Arjan Egges, Nicolas Pronost) • Many research projects • Gate • Commit • Commands • Thesis and PhD projects • Much interest from the industry

7. Practical aspects • Meetings • Tuesday 9.00 - 10.45 in UNNIK-517 • Thursday 15.15 - 17.00 in ISRAELS-002 (Israëlslaan 118) • Presence is mandatory • If you cannot come for whatever reason • Hand in abstracts (on paper) during the meeting • Website • http://www.cs.uu.nl/docs/vakken/mpap/ • Check the schedule • Check regularly for announcements and changes • Download papers • Find the secret page

8. Assignments • Present two papers • Contents (10 min.), critical review (15 min.), discussion (15 min.) • Write paper abstracts/assessments • Only read the paper before your presentation • One page per paper • Short 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? • Three questions or points for discussion • Hand in the two pages (on paper) on the day of the presentation • Use headings: Summary, Assessment, Questions

9. Assignment 1 • Study path planning/crowd simulation in a modern game • Deliberately try to create problems • Destroy objects/buildings • Stand in the way of moving characters • Park a car on the sidewalks • Let a character follow you while traversing a `difficult route’

10. Assignment 1 • Collect video footage of issues that go wrong due to • Collision-avoidance with entity, group of entities, or obstacle; e.g. a character • gets blocked; • takes an illogical detour; • does not avoid congested areas; • passes through an obstacle or another character; • makes sudden undesired directional changes; • does not look ahead; • walks through a group of interacting characters. • Poor path planning and animation; e.g. a character • collides with another animated character; • takes an illogical detour (e.g. a shorter path exists that requires jumping or climbing); • takes an illogical action (e.g. it may walk through an object while jumping would be more logical).

11. Assignment 1 • Procedure • Investigate what goes wrong • Make one video • Use a 720p or 1080p resolution if possible; • Compress your movie, and use a high bit-rate for the movie; • Convert the video to a WMV-file • Use e.g. Fraps for recording the movie • You may include multiple examples • Don't use transitions (e.g. fades) between clips or overlays (e.g. text); • Present 3 slides next Tuesday (September 16) for discussion • Name of the game, your names, picture, type of game • The video • Description of what goes wrong and why (according to you) • Take with you on USB stick • Explain and discuss (5 minutes)

12. Some results of a previous assignment

13. Assignment 2 • Choose between writing a paper or implementation • Deadline • November 4 (23.59) • Use the Submit system • Paper on path planning/crowd simulation in games • Based on the problems extracted from assignment 1 • Write a paper (10 pages) on how these problems can be solved • Audience: a recommendation for the programmer of the game • Try to be as critical on your own paper as you were on the paper you have reviewed • Implement the RVO2 collision-avoidance method into our Crowd simulation framework • C++ • Compare this method with two other (already integrated) methods • A fair comparison is important • Write a report

14. Grading • Game study 5% • Presentations 20% + 20% • Abstracts 30% • Paper/implementation 25% • To qualify for second change exam • The original mark should at least be a 4; • Actively participate in at least 13/17 of the meetings; • Write at least 20/26 abstracts; • Give both presentations satisfactory.

15. Grading • You cannot pass the course if you skip • assignment 1 or 2; • one of your presentations; • 5 or more meetings (out of 17 meetings); • 7 or more abstracts (out of 26 abstracts).

16. Workshops • Workshop 1 • September 18 • Crowd simulation • Software and framework • Collision-avoidance algorithms • C++ • Experimental research • Workshop 2 • November 4 • A* Search • Understand the A* algorithm • Reason about its properties • Apply it to a range of problems

17. Schedule

18. Path planning • Goal: bring characters from A to B through an environment • Also vehicles, animals, camera, a formation, … • Requirement: fast and flexible • Real-time planning for thousands of characters • Individuals and groups • Dealing with local hazards • Different types of environments

19. Path planning • Goal: bring characters from A to B through an environment • Also vehicles, animals, camera, a formation, … • 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 • For example, the way humans move • Low energy usage (smooth, short, minimal rotation/acceleration) • Keep some distance (clearance) to obstacles • Social behavior and rules (collision avoidance) • …

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

21. Do we need a new path planning algorithm? 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 Correctness fool-proof may be incorrect

22. Path planning errors in games

23. Path planning errors in games • 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

24. What we study in the course • Methodology/framework that solves 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? • Must a path planning algorithm compute a path? • Crowd behavior • Flow models • Planning approaches • Massive crowds • Crowd evaluation

25. 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 to obstacles • Computed efficiently by using the GPU or CPU Explicit Corridor Map (2D) Explicit Corridor Map (multi-layered)

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

27. 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

28. The Indicative Route Method [meso scale] • The Indicative Route Method • 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 • It uses a corridor • Provides a global route • Allows for flexibility

29. The Indicative Route Method [meso scale] • “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

30. 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 that oscillations are avoided • 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

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

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

33. 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

34. 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

35. Improved collision-avoidance model • Collision-prediction 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

36. 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 forming groups • Fast (thousands of characters in real time) Helbing Collision prediction

37. Improved collision-avoidance model

38. Improved collision-avoidance model

39. Some previous work • Also allow speed changes • Deal with small groups

40. Further work • Get different types of high-level crowd behavior • Social behavior • Collective behavior • Incorporating semantics • … • Combine different types of moving entities • People • Bikes • Cars • Animals • Combination of path planning and animation in 3D