Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Crowd simulation

Website:website containing additional information
Course code:INFOMCRWS
Credits:7.5 ECTS
History:This course was formerly known as Path planning (INFOMPAP). You can only do one of these courses.
Period:period 2 (week 46 through 5, i.e., 13-11-2017 through 2-2-2018; retake week 16)
Timeslot:A
Participants:up till now 29 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Mon 11.00-12.4546-47 DDW-0.42 CLZ Roland Geraerts
Wouter van Toll
   
48-50 BBG-165
51 RUPPERT-C
2 BBG-165
3 RUPPERT-C
4 BBG-165
Wed 9.00-10.4546-51 BBG-165
2-4 BBG-165
Contents:A huge challenge in computer games and other applications is to simulate large crowds of moving characters in a virtual environment in real-time. These characters need to avoid collisions with obstacles and with other characters. Also, it is important that their paths are visually compelling or even realistic (depending on the application).

In this course, we will study and discuss state-of-the-art research papers on path planning and crowd simulation, and we will analyze how to apply these techniques in computer games.

Literature:A selection of research papers on path planning and crowd simulation is available on the course website ("Literature"). You will give a presentation on one of them, and write summaries and critical reviews of the other papers. We will discuss these papers during our meetings.

Course form:This course is a seminar with regular mandatory meetings. In most of these meetings, you will present and discuss research papers. There are also a few standard lectures, and some sessions in which you will present assignment results. See the course website for a detailed schedule.

There will be various other assignments next to the presentations and abstracts mentioned before. In these assignments, you will study crowd simulation problems in current games, and you will create your own game in which you try to solve these problems.

Exam form:There is no written exam. Your final grade for this course will be a weighted average of the assignments' grades. The exact weighting rules can be found on the course website ("Assignments").

Minimum effort to qualify for 2nd chance exam:The rules for qualifying for a retake exam can be found on the course website ("Assignments").

Description:This course has the following learning goals. After having passed the course, the student:
  • knows the basic path planning problem, and knows how to translate these path planning problems to configuration space;
  • knows about several approaches to path planning (e.g. sampling, roadmaps, cell decompositions) and is aware of the advantages and drawbacks of each of the methods;
  • knows about modern navigation meshes for representing the walkable space in virtual environments (such as triangulations, voxel-based methods, Explicit Corridor Maps), and is aware of the advantages and drawbacks of each of the methods;
  • knows how to compute shortest paths in graphs (using A*/Dijkstra), and understands how this translates to smooth paths in virtual environments;
  • knows about extensions to basic motion planning (such as multiple robots, combinatorial motion planning, dynamic environments);
  • knows about different methods for collision avoidance (such as Helbing's model, RVO, vision-based steering);
  • knows how to model crowd flows and streams and how to deal with massive crowds;
  • knows how to model different behaviors in crowds (such as small and large groups, personalities, panic);
  • knows how crowd simulation software can be structured, and is aware of its implementation issues (such as computational geometry problems, speed considerations, multi-core processing);
  • knows about several crowd simulation applications (such as evacuation, safety, training, games);
  • has insight into the state-of-the-art research in path planning and crowd simulation, and knows which problems are still open;
  • and is able to interpret, critically assess, and compare state-of-the-art results in the field;
  • knows how to critically look at path planning and crowd simulation in games;
  • knows how to actively lead (and participate in) scientific discussions and how to give better presentations.
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