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 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)
Timeslot:B
Participants:up till now 0 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Roland Geraerts
tutorial group 1        Mees van de Kerkhof
Jérôme Urhausen
Note:No up-to-date course description available.
Text below is from year 2019/2020
Contents:A huge challenge in computer games and other applications is to simulate large crowds of moving agents in a virtual environment in real-time. These agents 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 applications that need realistic crowds.

Literature:May change!
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 online meetings.

Course form:This course is a seminar with regular mandatory online 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 work in small group on a selected problem related to crowds.

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:To qualify for the retake exam, the grade of the original must be at least 4.
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, and personalities);
  • 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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