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Planning and Decision-Making for Mobile Robotics

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  • Lecturer: Anis SAHBANI (anis.sahbani@sorbonne-universite.fr)
  • Course code:
  • Student workload: 12 h of lectures, 8 h of labs
  • Credits: 3 ECTS
  • Specialization tracks: ROBOT (elective) SI (elective) MeDH
  • Semester offered: S1 S2 S3 S4
  • Language of instruction: French English
  • Targeted audience: Eng. Sc. department Other :
  • Localization : PMC Campus Other :

Course Overview

This course provides students with the knowledge needed to select software tools for designing a robotic platform or performing a task. Students learn the guiding concepts behind these tools and their current or potential applications in robotics. The course introduces the full range of robotics algorithms, from localization and navigation to control, planning and reasoning.

Mots-clés : Localization, navigation, planning and decision-making in robotics

Prerequisites

Students should have previously acquired the following prerequisites to follow this course:

  • Digital filtering
  • Probability
  • Python
  • C++
  • ROS 1/ROS 2

Intended Learning Outcomes

By the end of this course, students will be able to:

  1. Understand planning problems in robotics, the course framework and objectives, and the relationship with the perception and control course units
  2. Analyze localization in mobile robotics (incremental localization with a Kalman filter, Markov localization, etc.)
  3. Compare different mapping and SLAM methods (visual, particle-filter-based, cooperative, etc.)
  4. Categorize different navigation strategies (topological, metric, reactive, local, global, etc.)
  5. Solve motion-planning problems: configuration space and exact, heuristic and probabilistic planning methods
  6. Solve task-planning and decision-making problems in robotics: representation, state space, partial-plan space, Graphplan and nondeterministic planning
  7. Develop a program to plan the motion of a mobile robot in a dynamic environment with a moving target using the potential-field method (TP1)
  8. Develop a task-sequencing program using symbolic planning (TP2)
  9. Design and implement a strategy that enables a mobile robot to autonomously map the area around the practical room and navigate within the resulting map while accounting for moving obstacles (Project)

Indicative Teaching Sequence and Methods

Week C/TD/TP* Content Preparation Learn.\ outc.
S1 C1+C2 Introduction to planning + localization + mapping AAV1, AAV2,AAV3
S2 C3+C4 Navigation strategies + motion-planning methods AAV1, …, AAV3 AAV4, AAV5
S3 TP1 Potential-field method AAV1, …, AAV5 AAV7
S4 C5 planification symbolique AAV6
S5 TP2 Plan generation AAV6 AAV8
S6 Project Mapping and autonomous navigation AAV1, …, AAV8 AAV9
S7 Project Mapping and autonomous navigation AAV1, …, AAV8 AAV9
S8 Project Mapping and autonomous navigation AAV1, …, AAV8 AAV9
  • C/TD/TP respectively corresponds to lectures, tutorials and lab sessions.

Indicative Assessment of Intended Learning Outcomes (1st session)

Week Individ./group In-person/remote Type of exam Evaluated outcomes Scale %
S3 Individual In-person TP AAV1, …, AAV5 15%
S5 Individual In-person TP AAV7 15%
S8 Individual In-person Other AAV1, …, AAV8 30%
S9 Individual In-person Written exam AAV1, …, AAV8 40%

2nde session

Session Individ./group In-person/remote Type of exam Evaluated outcomes Scale %
2 Group In-person Written exam AAV 1-10 50%
1 Group In-person Quiz AAV1-8 50%

Bibliographic references

  • Introduction to Autonomous Mobile Robots, R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza, MIT press, 2e éd., 2011
  • Robotics: Modelling, Planning and Control, B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Springer,, 2009
  • State Estimation for Robotics, T.D. Barfoot, Cambridge University Press, 2017
  • Automated Planning: Theroy and Practice, M. Ghaleb, D. Nau, P. Traverso, Elsevier - MKP, 2004
  • Planning Algorithm, S. M. LaValle, Cambridge University Press, 2006

Logo SDI Date of generation of this unit description: 06/10/2026 Logo SDI