Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
9105095102012Agricultural Robots Elective128
Level of Course Unit
Second Cycle
Objectives of the Course
The course introduces the basic concepts in automated vehicles (field robots, semi-autonomous agricultural machines), focusing on their use in agriculture, and illustrations of current state of the art in terms of research, applications, and prototypes in agricultural automation domain.
Name of Lecturer(s)
Doç. Dr. Arif Behiç TEKİN
Learning Outcomes
1be able to evaluate the functionality and working dependable operating characteristics for sensors, navigation systems such as Global Navigation Satellite Systems (GNSS) and Local Positioning Systems (LPS), systems for energy control, system for control and surveillance
2be able to understand embedded cognition (sensing inference) for optimal use of robotics and automation
3be able to consider the selection of system and component in relation to the requirements and differences in the applications
4be able to understand and explain the multi-disciplinary aspect of automated vehicles in bio-production systems
5be able to identify the specific features of a bio-production oriented automated vehicle
6be able to understand and explain the limitations of the existing platforms, prototypes, and architectures and the challenges that these limitations impose
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Fundamental concepts in robotics and automation including topics such as locomotion, perception, localization, navigation, planning, sensors and sensors integration, planning and control, will be tackled oriented to the case of dynamic and unpredictable environments. Finally aspects regarding scaling up single-unit systems to multiple-unit systems are discussed
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Introduction and basic knowledge on course and methods to be followed for teaching
2Existing mobile robotic platforms, prototypes and architectures in bio-production systems
3Current advances in automation systems for manned agricultural vehicles (auto-steering systems, vision-based navigation systems, communication and information systems for agricultural vehicles)
4Sensor systems: Sensors supporting agricultural vehicles in relation to bio-production tasks that they have been designed to carry out (e.g., weed detection, yield prediction, soil properties measurements, etc.)
5Navigation systems: Sensors and sensors integration
6Navigation systems: Algorithms (i.e., for path tracing, localisation, obstacle avoidance, etc.)
7Navigation systems: Algorithms (i.e., for path tracing, localisation, obstacle avoidance, etc.)
8Midterm exam
9High level control: Geometric interpretation of the world
10High level control: Path planning (area coverage planning, motion and route planning methods and algorithms for agricultural vehicles)
11High level control: Path planning (area coverage planning, motion and route planning methods and algorithms for agricultural vehicles)
12High level control: Mission planning for field robots
13High level control: Mission planning for field robots
14High level control: Mission planning for field robots
15Presentations and evaluation of the course
16Final Exam
Recommended or Required Reading
Dudek G; Jenkin M (2000). Computational Principles of Mobile Robotics. Cambridge University Press. Mas, F. R., Zhang, Q. and Hansen, A. C., (2010). Mechatronics and Intelligent Systems for Off-Road Vehicle. Springer-Verlag London Limited. New York. laValle S. M. (2006). Planning algorithms. Cambridge University Press.
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight
SUM0
End Of Term (or Year) Learning ActivitiesQuantityWeight
SUM0
SUM0
Language of Instruction
Turkish
Work Placement(s)
None
Workload Calculation
ActivitiesNumberTime (hours)Total Work Load (hours)
Midterm Examination11212
Final Examination11515
Attending Lectures14342
Team/Group Work31030
Project Preparation14570
Project Presentation133
Writing Paper11010
Homework51050
TOTAL WORKLOAD (hours)232
Contribution of Learning Outcomes to Programme Outcomes
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
LO1523544
LO2445352
LO3533444
LO4454535
LO5545555
LO6      
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr