2023
Robotic Systems
Name: Robotic Systems
Code: EME13197M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Electrotechnical Engineering, Mechanical Engineering
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
The student will consolidate his knowledge in robotic systems (Manipulators and AGVs). kinematics and Dynamical models, Robotic Sensors and Actuators.
The student will gain special know-how in vision-sensors. The student will have the capability to implement an image processing system, in the domain of pattern recognition. The vision-sensor information is processed and the decision is send to a PLC that will control the corresponding actions.
The student will gain special know-how in vision-sensors. The student will have the capability to implement an image processing system, in the domain of pattern recognition. The vision-sensor information is processed and the decision is send to a PLC that will control the corresponding actions.
Contents
1) Manipulator robots. Robot classes. Components of a robotic system.
2) Mathematical models of typical joints. Kinematic chains. Kinematics and linear transformations: direct kinematics and inverse kinematics.
3) Robot Dynamics: Lagrange and Newton-Euler formulations.
4) Robot Control: independent joint-control, work space-control, gripper position and force control.
5) Mobile Robots.
6) Robotic sensors: position/speed, proximity, force/torque, artificial vision sensors.
7) Introduction to automatic vision. Equipment for industrial vision. Digital signal processing. Filtering. Textures and form classification. Introduction to pattern recognition.
8) The integration of artificial vision in industrial automation controlled by PLC (Programmable Logic Controller). Practical implementations with vision sensors Siemens VS-710 (Siemens-ProVision).
2) Mathematical models of typical joints. Kinematic chains. Kinematics and linear transformations: direct kinematics and inverse kinematics.
3) Robot Dynamics: Lagrange and Newton-Euler formulations.
4) Robot Control: independent joint-control, work space-control, gripper position and force control.
5) Mobile Robots.
6) Robotic sensors: position/speed, proximity, force/torque, artificial vision sensors.
7) Introduction to automatic vision. Equipment for industrial vision. Digital signal processing. Filtering. Textures and form classification. Introduction to pattern recognition.
8) The integration of artificial vision in industrial automation controlled by PLC (Programmable Logic Controller). Practical implementations with vision sensors Siemens VS-710 (Siemens-ProVision).
Teaching Methods
The teaching method is based on theoretical classes and practical classes. An active learning system is focused to stimulate the student to make his own research on the matters presented in the classes.
Practical problems are solved in the classroom, with laboratory experimentation, what allow the students to identify and to become familiar with the studied industrial technology.
The assessment method consists of two components:
P1= Experimental project on manipulation robots;
P2= Experimental project on artificial vision - Pattern Recognition - integrated in Industrial Automation environment (PLC),
Or
P2= Experimental project on mobile robots.
The final grade is calculated by: 0,5*P1+0,5*P2
Practical problems are solved in the classroom, with laboratory experimentation, what allow the students to identify and to become familiar with the studied industrial technology.
The assessment method consists of two components:
P1= Experimental project on manipulation robots;
P2= Experimental project on artificial vision - Pattern Recognition - integrated in Industrial Automation environment (PLC),
Or
P2= Experimental project on mobile robots.
The final grade is calculated by: 0,5*P1+0,5*P2
Teaching Staff
