English

If sufficient demand

The aim of the course is to study methods for automated motion planning and related approaches for control design. A particular focus in the course is to use the studied methods in practice and how the algorithms could be used as one component in a larger hierarchical control system.

Introduction to Motion Planning and Control, Fundamental Concepts in Motion Planning, Sampling-Based Methods in Motion Planning, Combinatorial Motion Planning, Motion Planning for Time-Varying and Hybrid Systems, Introduction to Motion Planning and Feedback Control, Sampling-Based and Optimization-Based Motion Planning with Differential Constraints, Methods from System Theory and Analytical Techniques for Motion Planning and Control, Reinforcement Learning for Motion Planning and Control.

have a deep understanding and knowledge of modern algorithms and methods for motion planning, both for feasible planning and optimal planning.
understand how these methods could be used together with strategies for achieving robustness during execution of the plan by integration of feedback. understand how the parameters of the different algorithms influence the resulting motion plan and how they interact with different kinds of system dynamics.

be able to implement state-of-the-art methods for motion planning and control and to establish an appropriate modeling approach for the problem at hand and tune the parameters of an algorithm to a specific application.
be able to select an appropriate method for a specific task and integrate it in a larger hierarchical control architecture.
be familiar with the latest research within the field and the ability to read scientific papers about new methods in the field and then realize the method in an actual implementation.

Lectures

Seminars

Project

Written report

Written assignments

Seminars given by participants

In order to receive course credits, the participant is required to:
* Attend the weekly meetings and actively take part in the discussions during the seminars.
* Submit the hand-in assignments prior to each meeting (primarily implementation code or scripts with comments, no extensive written reports required).
* Prepare and give one, or at most two, lectures during the course.
* Complete a mini project, present it at a project seminar and present the results in a written report.

Failed, pass

LaValle, Steven M.: Planning Algorithms. Cambridge University Press, Cambridge, UK, 2006.

The book is complemented with several scientific papers and book chapters.

FRT180F

 -12-07

Professor Thomas Johansson