Moses - Motion Planning

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Module / Version:
#41049 / #2

Validity:
Seit SoSe 2023

Default display language:
English

Module title:
Motion Planning

Credits:
6

Responsible person:
Hönig, Wolfgang

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Technische Informatik und Mikroelektronik

Area of expertise:
34342100 FG Intelligent Systems

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-4

Contact person:
Hönig, Wolfgang

E-mail address:
hoenig@tu-berlin.de

Website:

https://imrclab.github.io/teaching.html

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 Name	Type	Number	Cycle	Language	SWS	VZ
Motion Planning	IV		SoSe	German	4

Workload and Credit Points

Motion Planning (IV):

Workload description	Multiplier	Hours	Total
Attendance	15.0	4.0h	60.0h
Pre/post processing	15.0	8.0h	120.0h
			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

Name	Points	Categorie	Duration/Extent
Homework Assignment or Discussion each week (up to 1 point each week; up to 10 points total)	10	flexible	Presentation of homework solution during discussion or written homework solution
4 Programming Assignments (10 points each)	40	practical	Sourcecode (number of lines depending on language used)
Written exam	50	written	60 min

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	86.0pt	82.0pt	78.0pt	74.0pt	70.0pt	66.0pt	62.0pt	58.0pt	54.0pt	50.0pt

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 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

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)	1	4	SoSe 2023	WiSe 2024/25
Computer Engineering (M. Sc.)	1	4	WiSe 2023/24	SoSe 2024
Elektrotechnik (M. Sc.)	1	4	WiSe 2023/24	SoSe 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.

Motion Planning