Duration
18h Th, 18h Pr, 6h AUTR
Number of credits
| Master in environmental bioengineering, professional focus | 4 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course addresses the fundamental concepts of autonomous mobile robot navigation applied to environmental monitoring and precision agriculture. Through a practical case study in robotic phenotyping (characterizing grassland biomass using a wheeled ground robot), the course covers:
- Kinematic modeling of mobile robots in unstructured natural environments;
- Spatial coverage path planning;
- Accounting for and modeling environmental uncertainties;
- Localization principles and sensor fusion;
- Quantitative evaluation of the impact of navigation errors on the quality of generated environmental metrics.
Learning outcomes of the learning unit
By the end of this course, students will be able to:
- Model the kinematics of a wheeled mobile robotic platform operating in a natural environment;
- Plan and program an optimized spatial coverage path for an environmental sampling or mapping mission;
- Simulate and inject various types of uncertainties and sensor noise representative of real-world natural constraints;
- Apply sensor fusion algorithms to estimate the robot's position and reduce localization bias;
- Quantify and critically evaluate the impact of autonomous navigation errors on the accuracy of agro-environmental variable maps (e.g., biomass estimation);
- Independently develop a complete, well-documented numerical solution in the form of a Python script or notebook.
Prerequisite knowledge and skills
Basic notions in mechanics, system dynamics and instrumentation
Planned learning activities and teaching methods
- Integrated problem-based learning project: Execution of a complete numerical simulation project, structured in progressive milestones (from the ideal noise-free case to sensor fusion under high uncertainty).
- Guided reading and self-directed learning: Independent study of targeted chapters from the reference textbook (Robotics, Vision and Control, P. Corke) and associated Python documentation.
- Critical analysis of results: Interpretation of the generated mapping data and discussion of the agro-environmental impact of measurement biases.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
- Format: Project-based learning with guided autonomy.
- Supervision: Support provided through tutoring sessions.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Exam(s) in session
Any session
- In-person
oral exam
Continuous assessment
Work placement(s)
Organisational remarks and main changes to the course
Contacts
benoit.mercatoris@uliege.be