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General information

Academic year:
2024
Description:
Master's thesis on intelligent field robotic systems
Academic credits:
30
Course coordinator:
Nuno Ricardo Estrela Gracias

Groups

Group J

Duration:
One-semester, 2nd semester
Teaching staff:
Patryk Andrzej Cieslak  / Nuno Ricardo Estrela Gracias  / Josep Forest Collado  / Rafael Garcia Campos  / Narcis Palomeras Rovira
Language of the classes:
English (100%)

Group S

Duration:
One-semester, 2nd semester
Teaching staff:
Patryk Andrzej Cieslak  / Nuno Ricardo Estrela Gracias  / Josep Forest Collado  / Rafael Garcia Campos  / Narcis Palomeras Rovira
Language of the classes:
English (100%)

Competences

  • CG1- Organize and evaluate the learning and the research activity themselves and develop strategies to improve them
  • CG3- Communicate in an effective way both orally and in writing, preparing documents and presenting projects and results with English language
  • CG4- Designing creative proposals
  • CG5- Collect and select information to be able to evaluate the state of the art of a specific topic or subject
  • CB7- That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader contexts related to their area of ??study
  • CB8- That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments
  • CB9- That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialized audiences in a clear and unambiguous way
  • CE1- Programming, at an advanced level, in the languages and libraries most used in intelligent field robotics
  • CE2- Analyse a problem related to intelligent autonomous systems and identify the appropriate techniques and tools to solve it
  • CE3- Understand, develop, modify and effectively apply machine learning methods.
  • CE4- Know the code of ethics in the exercise of the profession as well as the ethical principles related to new technologies
  • CE5- Know, understand and be able to apply the algorithms that allow autonomous vehicles to localize themselves and navigate effectively
  • CE6- Know and understand when and how to use the main sensors and actuators available for intelligent field robots
  • CE7- Understand and be able to apply the main computer-based perception techniques
  • CE8- Understand the mathematical foundations of intelligent robotic system algorithms
  • CE9- Design and manage projects in the field of intelligent field robotic systems
  • CE10- Learn and use the main techniques of control and trajectory planning used in manipulators and autonomous vehicles

Other competences

  • CE 11 - Realitzar, presentar i defensar davant un tribunal universitari, un projecte d'enginyeria en l'àmbit dels Sistemes Robòtics Intel·ligents en el que es sintetitza les competències adquirides a les ensenyances.

Syllabus

1. Master Project development

2. Thesis preparation and defense

Activities

Activity type Hours with a teacher Hours without a teacher Virtual hours with a teacher Total
Individual preparation of assignments 25,00 725,00 0 750,00
Total 25,00 725,00 0 750

Bibliography

    Assessment and Grading

    Assessment activities:

    Description of the activity Assessment Activity % Remediable subject
    Development and presentation of a Master Thesis Students will be evaluated using the following inputs:
    1. Supervisor (40%). The supervisor will evaluate the work of the student in terms of quality, management, performance, and the dissertation document;
    2. Second Reader (30%). The dissertation document will be evaluated by a second reader;
    3. Presentation assessment (30%). The examination board will evaluate the oral presentation and the questions answered by the student.
    100 No

    Grading

    Students will be evaluated using the following inputs:
    a. Supervisor (40%). The supervisor will evaluate the work of the student in terms of quality, management, performance, and the dissertation document.
    b. Second Reader (30%). The dissertation document will be evaluated by a second reader. The assignment will be done by the supervisor and the coordinator, according to the topic and expertise of the reader.
    c. Presentation assessment (30%). The examination board will evaluate the oral presentation (presentation skills, structure, results, methodology, etc.) and the questions answered by the student.

    Specific criteria for the "No show" grade:
    "No Presentat" will be given to a student not attending the Thesis defense presentation.

    Single Assessment:
    Given the type of activity involved in the development and presentation of a thesis, the Avaluació Única is identical to the normal evaluation procedure.

    Minimum requirements to pass:
    A minimum grade of 5.0 must be obtained to pass this course.
    Any fraudulent action or plagiarism detected during the project development or at the time of its defense, will lead to failure in passing the course.

    Mentorship

    Students can arrange tutorial sessions with the professor by contacting the professor via email. Whenever possible, questions and doubts will be solved via email. Otherwise the tutorial will be conducted using Google Meet or face-to-face.

    Communication and interaction with students

    Presentation of information about the course and course activities will be done through Moodle. Google Meet or Teams will be used for non-contact sessions. All message communication between the professors and the students will be made by internal Moodle messaging system or by email. Students will use Moodle to upload reports.

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