2024
Robotics
Name: Robotics
Code: FIS13021O
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mechatronic Engineering
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Sustainable Development Goals
Learning Goals
• The student will consolidate his knowledge in robotic systems. 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. Classification and components of a robot.
2) Mathematical models of typical joints. Kinematics and linear transformations: direct kinematics and inverse kinematics.
3) Robot Dynamics.
4) Robot Control: independent joint-, work space, gripper position and force.
5) Robotic sensors: position/speed, proximity, force/torque, artificial vision sensors.
6) Equipment for industrial vision. Digital signal processing. Filtering. Textures and form classification. Introduction to pattern recognition.
7) Artificial vision in industrial automation controlled by PLC. Practical implementations with Siemens VS-710.
2) Mathematical models of typical joints. Kinematics and linear transformations: direct kinematics and inverse kinematics.
3) Robot Dynamics.
4) Robot Control: independent joint-, work space, gripper position and force.
5) Robotic sensors: position/speed, proximity, force/torque, artificial vision sensors.
6) Equipment for industrial vision. Digital signal processing. Filtering. Textures and form classification. Introduction to pattern recognition.
7) Artificial vision in industrial automation controlled by PLC. Practical implementations with Siemens VS-710.
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 on 2 projects:
P1= Experimental project on manipulation robots;
P2= Experimental project on artificial vision - Pattern Recognition;
The final grade is calculated by: (P1 + P2)/2
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 on 2 projects:
P1= Experimental project on manipulation robots;
P2= Experimental project on artificial vision - Pattern Recognition;
The final grade is calculated by: (P1 + P2)/2
