Competencies and objectives

 

Course context for academic year 2024-25

Robots are autonomous physical systems operating in dynamic environments and complex scenarios. An increasing variety of robots are being equipped with capabilities to perform tasks previously only available to humans. Robots can now move and localise themselves, perform tasks such as grasping and manipulating objects, interact with other elements of the environment or assist humans. Sensors and AI techniques have enhanced these skills, allowing robots to learn and adapt.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

Transversal Competences

  • CT1 : Be able to lead projects related to artificial intelligence, as well as to manage work teams.
  • CT2 : Demonstrate computer and information skills in the field of artificial intelligence.
  • CT3 : Show oral and written communication skills.

 

General Competences

  • CG10 : Be able to use engineering principles and modern computer technologies to research, design and implement new applications of artificial intelligence,
  • CG2 : Be able to develop and learn in a self-directed or autonomous way topics related to artificial intelligence.
  • CG6 : Being able to adapt to environments related to artificial intelligence, fostering teamwork, creativity,
  • CG7 : To be able to adapt to the constant evolution of the discipline and to understand and apply new technical and scientific developments related to artificial intelligence.

 

Specific Competences

  • CE16 : Identify the sensory perception techniques needed to set up mobile robots and autonomous manipulators.
  • CE17 : Develop processing methods for data from robotic sensory sources to achieve autonomous robots.

 

Basic Competences

  • CB10 : That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
  • CB6 : Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context
  • CB7 : That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ¿¿study
  • CB8 : Students are able to integrate knowledge and deal with the complexity of making judgements on the basis of incomplete or limited information, including reflections on the social and ethical responsibilities associated with information which, while incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgements.
  • CB9 : Students are able to communicate their conclusions and the ultimate knowledge and rationale behind them to specialist and non-specialist audiences in a clear and unambiguous way.

 

 

 

Learning outcomes (Training objectives)

-Obtain a critical vision of an intelligent robotic system and identify its components and functionalities according to its field of application.

-Acquire notions on the capture and processing of data from the different sensory sources that an intelligent robotic system is equipped with.

-Knowing the main techniques and methods aimed at providing robotic systems with the skills to achieve robust behaviour in realistic environments.

-Knowing how to propose solutions to robotic navigation and manipulation problems and their interaction with the environment.

 

 

Specific objectives stated by the academic staff for academic year 2024-25

  • To obtain a critical vision of an intelligent robotic system and identify its components and functionalities according to its field of application.
  • To acquire knowledge of data capture and data processing from the various sensory sources with which an intelligent robotic system is equipped.
  • To know the main techniques and methods used to provide robotic systems with capabilities to achieve robust behaviour in realistic environments.
  • To know how to propose solutions to problems of robot navigation and manipulation and their interaction with the environment.
  • To understand the basic processes such as localisation, perception, mapping and planning required for autonomous navigation, and the techniques and use of common algorithms to enable a robot to move autonomously and safely in its environment.
  • To understand the principles and techniques of robotic manipulation and know how to apply them to grasping and/or manipulation tasks that require working with perception of the environment.
  • To understand the basic principles of human-machine interaction in the context of robotics and apply this knowledge to the design and development of robotic projects involving effective human-machine interaction.

 

 

General

Code: 43507
Lecturer responsible:
Gil Vazquez, Pablo
Credits ECTS: 4,50
Theoretical credits: 0,90
Practical credits: 0,90
Distance-base hours: 2,70

Departments involved

  • Dept: PHYSICS, ENGINEERING SYSTEMS AND SIGNAL THEORY
    Area: SYSTEMS ENGINEERING AND AUTOMATICS
    Theoretical credits: 0,6
    Practical credits: 0,6
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.
  • Dept: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    Area: CIENCIA DE LA COMPUTACIO, INTEL·LIGENCIA ARTIFICIA
    Theoretical credits: 0,3
    Practical credits: 0,3

Study programmes where this course is taught