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#41049 / #2

Seit SoSe 2023

English

Motion Planning

6

Hönig, Wolfgang

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342100 FG Intelligent Systems

No information

Kontakt


MAR 4-4

Hönig, Wolfgang

hoenig@tu-berlin.de

Learning Outcomes

Motion planning is a fundamental building block for autonomous systems, with applications in robotics, industrial automation, and autonomous driving. After completion of the course, students will have a detailed understanding of: • Formalization of geometric, kinodynamic, and optimal motion planning; • Sampling-based approaches: Rapidly-exploring random trees (RRT), probabilistic roadmaps (PRM), and variants; • Search-based approaches: State-lattice based A* and variants; • Optimization-based approaches: Differential Flatness and Sequential convex programming (SCP); • The theoretical properties relevant to these algorithms (completeness, optimality, and complexity). Students will be able to: • Decide (theoretically and empirically) which algorithm(s) to use for a given problem; • Implement (basic versions) of the algorithms themselves; • Use current academic and industrial tools such as the Open Motion Planning Library (OMPL).

Content

This course is jointly developed and held by Dr. Andreas Orthey (Realtime Robotics) and Dr. Wolfgang Hönig (TU Berlin). It provides a unified perspective on motion planning and includes topics from different research and industry communities. The goal is not only to learn the foundations and theory of currently used approaches, but also to be able to pick and compare the different methods for specific motion planning needs. An important emphasis is the consideration of both geometric and kinodynamic motion planning for the major algorithm types. Part 1: Foundations • Introduction, Motivation, and Problem Formulation • Configuration space, Transformations, Angular representations, Metrics • Efficient collision checking Part 2: Search-Based • A* and relevant variants with their theoretical properties • Motion primitives, state-lattice-based planning • Search-based Planning Library (SBPL) Part 3: Sampling-Based • Tree-based planner: RRT, EST • Roadmap-based planner: PRM • Asymptotically-optimal sampling planner: RRT*; PRM* • Sampling theory (dispersion, discrepancy) • Open Motion Planning Library (OMPL) Part 4: Optimization-Based • Overview of continuous constrained optimization formulations • Parametric trajectory representation and differential flatness • Mathematical encoding of motion planning problems: SCP and KOMO Part 5: Current and Advanced Topics, e.g., • Realtime motion planning • Hybrid search-, sampling-, or optimization-based motion planning • Machine learning-based motion planning • Multi-robot motion planning: dRRT, M*

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Motion PlanningIntegrierte VeranstaltungSoSeGerman4

Workload and Credit Points

Motion Planning (Integrierte Veranstaltung):

Workload descriptionMultiplierHoursTotal
Attendance15.04.0h60.0h
Pre/post processing15.08.0h120.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

• Weekly lectures • Weekly homework assignments and discussion sessions • 4 Programming Assignments

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

• Ability to program in C++ or Python • Knowledge of linear algebra and calculus

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

NamePointsCategorieDuration/Extent
Homework Assignment or Discussion each week (up to 1 point each week; up to 10 points total)10flexiblePresentation of homework solution during discussion or written homework solution
4 Programming Assignments (10 points each)40practicalSourcecode (number of lines depending on language used)
Written exam50written60 min

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt86.0pt82.0pt78.0pt74.0pt70.0pt66.0pt62.0pt58.0pt54.0pt50.0pt

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Unregelmässig.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Current information in the associated ISIS course.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Steven M. LaValle, Planning Algorithms, Cambridge University Press, 2006. Available for free at http://lavalle.pl/planning/.
Kevin M. Lynch and Frank C. Park, Modern Robotics, Cambridge University Press, 2017
Howie Choset et al., Principles of Robot Motion: Theory, Algorithms, and Implementations, A Bradford Book, 2005
Francesco Bullo and Stephen L. Smith, Lectures on Robotic Planning and Kinematics, 2022. Available for free at http://motion.me.ucsb.edu/book-lrpk.

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)13SoSe 2023SoSe 2024
Computer Engineering (M. Sc.)14WiSe 2023/24SoSe 2024
Elektrotechnik (M. Sc.)14WiSe 2023/24SoSe 2024

Miscellaneous

Suitable for MS and PhD students in fields that consider autonomous systems, including but not limited to computer science, electrical engineering, mechanical engineering, and aerospace engineering.