Objectives and competences
Course objectives:
- Upgrade knowledge in the field of mechatronics by presenting modern robotic systems.
- Define the meaning and the role of robotic systems in logistics.
- Explain the principles of operation of industrial, collaborative, mobile robots and drones.
- Explain the principles of operation of sensors, actuators and robotic grippers.
- Explain theoretical principles of modelling and control of robotic mechanisms.
- Explain components and layouts of flexible intralogistics processes.
- Explain components and principles of operation of robotic bin picking, assembling, sorting, palletizing and loading/unloading.
Student competences:
- Knows and understands the operation of various robotic systems and their major components.
- Knows, understands and is able to select suitable robotic systems and grippers when planning flexible intralogistics processes.
- Able to design and simulate a flexible intralogistics process.
- Knows how to simulate and implement basic robotic applications.
- Able to evaluate and select the optimal grippers for robotic bin picking, assembling, sorting, palletizing and loading/unloading.
Content (Syllabus outline)
1. Introduction, basic definitions, use of robotic systems in logistics.
2. Industrial and collaborative robots: history of robotics, basic definitions, basic components, structure of robot controller, robot configurations, coordinate systems, degrees of freedom, direct and inverse kinematic model, homogeneous transformations, sensors, actuators, grippers, development tools, robot programming, robot commissioning using 2D and 3D machine vision.
3. Mobile robots (autonomous and automated-guided vehicles): the use of mobile robots in logistics, configurations of drive and steering systems, sensors, actuators, safety laser scanners, robotic operating system (ROS), location systems, navigation systems, central control systems, virtual robotic experimental tools.
4. Unmanned aircrafts (drones): the use of drones in logistics, drone configurations, sensors, actuators, location systems, navigation systems, ground control stations, legislation.
5. Components and layouts of flexible production and logistics systems. Modeling and simulation of discrete event systems (DES). Modeling of DES using Petri nets.
Learning and teaching methods
Lectures: Students understand the theoretical frameworks of the course. Part of the lecture course is in a classroom while the rest is in the form of e-learning (e-lectures may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment).
Tutorials: Students enhance their theoretical knowledge and are able to apply it. Part of the seminar is in a classroom while the rest is in the form of e-learning (e- tutorials may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment).
Intended learning outcomes - knowledge and understanding
At the end of the course, the student is able to:
- Understand and explain in more detail principle of operation of various robotic systems and their components.
- Assess the potential of the use of robotic systems in intralogistics.
- Analyze, critically evaluate and select suitable robotic systems to improve intralogistics processes.
- Formulate mathematical models of robotic mechanisms.
- Use of computer tools for simulation and program of robotic systems.
- Model and simulate discrete event systems.
- Design, analyze and optimize intralogistics processes using modern robotic systems.
- Apply intralogistics processes (assembling, sorting, bin picking, palletizing, loading/unloading) using modern robotic systems.
Readings
Mihelj, M., Bajd, T., Ude, A., Lenarčič, J., Stanovnik, A., Munih, M., Rejc, J., & Šlajpah, S. (2018). Robotics (2nd ed.). Springer. doi:10.1007/978-3-319-72911-4
Lerher, T. (2022). Avtomatska vozila in mobilni roboti v intralogistiki (1. izd.). Univerzitetna založba. https://press.um.si/index.php/ump/catalog/book/640
Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots (2nd ed.). The MIT Press.
Prerequisits
No special requirements.
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
- Seminar (project) work 20%
- Lab. exercises 30%
- Written exam (theoretical knowledge) 50%
Successful completion of e-lectures and e-tutorials is a prerequisite for entering the exam.
