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Planning and Decision Making for Autonomous Robots
Last Updated: 2026-06-03 00:07:37
Abstract
Planning 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 ethical/regulatory aspects. This course will cover the algorithmic foundations of motion planning, with an eye to real-world implementation issues.
Objective
The 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.
Content
Discrete planning, shortest path problems. Planning under uncertainty. Game-theoretic planning. Geometric Representations. Steering methods. Configuration space and collision checking. Potential and Navigation functions. Grids, lattices, visibility graphs. Mathematical Programming. Sampling-based methods. Planning with limited information. Multi-agent Planning.
Resources
Lecture Notes
Course notes and other education material will be provided for free in an electronic form.
Literature
There is no required textbook, but an excellent reference is Steve Lavalle's book on "Planning Algorithms."
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Planning and Decision Making for Autonomous Robots | No time listed | 2 h weekly |
| exercise | Planning and Decision Making for Autonomous Robots | No time listed | 1 h weekly |
Offered In
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Core Courses (The Core Courses in the Master’s program Mechanical Engineering listed below are indicative and include courses designed by the Department at the Master's level. With the approval of the tutor, students may also select Master's-level courses offered by other departments at ETH. These courses will be marked as non-regular in the LAG, but their categorization as Core Courses is possible if included in the approved LAG.)
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Robotics (Only one of the two course units 263-5902-00L Computer Vision resp. 227-0447-00L Image Analysis and Computer Vision may be recognised for credits for the overall (CSE Bachelor and Master) study programmes. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits in the field of specialisation `Robotics' for the overall (CSE Bachelor and Master) study programmes. However, the other course unit may be recognised for a different category.)
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Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
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Deep Track Robotics (These courses can be credited either as a specialization subject or as an elective subject.)
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