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SoSe 2022 - WiSe 2022/23


Motion Planning


Hönig, Wolfgang


Schriftliche Prüfung


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342100 FG Intelligent Systems

No information


MAR 4-4

Hönig, Wolfgang


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) and probabilistic roadmaps (PRM); • Search-based approaches: State-lattice based A*; • Optimization-based approaches: Sequential convex programming; • 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).


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. 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 Part 3: Sampling-Based • Sampling theory (dispersion, discrepancy) • Tree-based planner: RRT, EST • Roadmap-based planner: PRM • Asymptotically-optimal sampling planner: RRT*; PRM* • Open Motion Planning Library (OMPL) Part 4: Optimization-Based • Overview of continuous constrained optimization formulations • Mathematical encoding of motion planning problems Part 5: Current and Advanced Topics • Realtime motion planning • Hybrid search-, sampling-, or optimization-based motion planning • Machine learning-based motion planning • Multi-robot motion planning: dRRT, M*

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Motion PlanningIntegrierte Veranstaltungk.A.English4

Workload and Credit Points

Motion Planning (Integrierte Veranstaltung):

Workload descriptionMultiplierHoursTotal
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 and homework assignments

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Ability to program in C++ and Python

Mandatory requirements for the module test application:

1. Requirement
Unbenoteter Übungsschein

Module completion



Type of exam

Written exam




120 min

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:
Keine Angabe.

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



Recommended literature
Steven M. LaValle, Planning Algorithms, Cambridge University Press, 2006. Available for free at http://lavalle.pl/planning/.

Assigned Degree Programs

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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.


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.