Robot motion planning

Lucia Pallottino (University of Pisa, Italy)

Period:

Monday, July 7, 2025 to Friday, July 11, 2025

Year:

2025

Credits:

3.00

Duration:

Objectives

This course provides a comprehensive overview of robot motion planning. It starts with fundamental concepts like discrete optimization and search algorithms and gradually progresses to advanced topics such as sampling-based planning and potential-based methods. The course covers both theoretical aspects and practical applications, providing students with a solid foundation in this field.

PhD students will develop skills in breaking down complex motion planning problems into smaller, manageable subproblems and to identify and evaluate different approaches to solving motion planning problems.

Program

Introduction to robot motion planning.
Discrete optimization and discrete dynamic programming
Introduction to motion planning in continuous spaces
Combinatorial planning algorithms
Sampling-based motion planning.
Potential-based planning.
Behaviour-based algorithms.

A minimum of 75% attendance is required.

Teaching methods

Slides and blackboard

Assessment methods

Small assignements during the course, final exposition of student of assigned tasks

Bibliography

Planning Algorithms, Steven M. LaValle, Cambridge University Press, 2006, available online

Schedule:

7 July 2025, 2 – 4:30 PM
8 July 2025, 10:30AM – 1PM; 2 – 4:30PM
9 July 2025, 10:30AM – 1PM; 2 – 4:30PM
10 July 2025, 10:30AM – 1PM;2 – 4:30PM
11 July 2025, 10:30AM – 1PM

Menù

Teaching methods

Assessment methods

Bibliography