151-0325-00L  Planning and Decision Making for Autonomous Robots

SemesterAutumn Semester 2021
LecturersE. Frazzoli
Periodicityyearly recurring course
Language of instructionEnglish


151-0325-00 VPlanning and Decision Making for Autonomous Robots2 hrs
Wed10-12HG E 3 »
E. Frazzoli
151-0325-00 UPlanning and Decision Making for Autonomous Robots1 hrs
Wed12-13HG F 1 »
E. Frazzoli

Catalogue data

AbstractPlanning safe and efficient motions for robots in complex environments, often shared with humans and other robots, is a difficult problem combining discrete and continuous mathematics, as well as probabilistic, game-theoretic, and learning aspects. This course will cover the algorithmic foundations of motion planning, with an eye to real-world implementation issues.
ObjectiveThe students will learn how to design and implement state-of-the-art algorithms for planning the motion of robots executing challenging tasks in complex environments.
ContentDiscrete planning, shortest path problems. Planning under uncertainty. Game-theoretic planning. Geometric Representations. Configuration space. Grids, lattices, visibility graphs. Sampling-based methods. Potential and Navigation functions. Mathematical Programming. Local and global optimization, convex relaxations. Planning with limited information. Multi-agent Planning.
Lecture notesCourse notes and other education material will be provided for free in an electronic form.
LiteratureThere is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."
Prerequisites / NoticeStudents should have taken basic courses in optimization, control systems, probability theory, and should be familiar with basic programming (e.g., Python, and/or C/C++). Previous exposure to robotic systems is a definite advantage.
Taught competenciesTaught competencies
Domain A - Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersE. Frazzoli
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 150 minutes
Additional information on mode of examinationThere is a written final exam during the examination session, which covers all material taught during the course, i.e. the material presented during the lectures and corresponding problem sets, programming exercises, and recitations.
Additionally, there will be programming assignments, which are an optional learning task during the semester, requiring the students to understand and apply the lecture material. These contribute a maximum of 0.25 grade points to the final grade, but only if it helps to improve the final grade.
Written aidsOne sheet of A4 paper, front and back. Only handwritten material by the individual student is allowed --- no computer printouts or photocopies. (Preparing such a sheet would be an important part of the learning process.)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

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Only public learning materials are listed.


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Offered in

Mechanical Engineering MasterRobotics, Systems and ControlWInformation
Robotics, Systems and Control MasterCore CoursesWInformation