Introduction to Robotics

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
ESAII
Since Robotics can be defined as the intelligent connection of perception to action, Artificial Intelligence must have a central role in this connection. Robotics challenges AI by forcing it to deal with real objects in the real world. This includes reasoning about space, path-planning, uncertainty and compliance, among others.
In this subject the basic knowledge about arm manipulators and mobile robots on the market that allow their operation, control and programming is exposed, with focus on the fields of perception, planning and action. The main areas of application of robotics and their demands are presented, both in the industrial and service fields.

Teachers

Person in charge

  • Anais Garrell Zulueta ( )

Others

  • Joan Aranda López ( )

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Transversals

  • CT1 [Avaluable] - Entrepreneurship and innovation. Know and understand the organization of a company and the sciences that govern its activity; Have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit.
  • CT2 [Avaluable] - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT8 [Avaluable] - Gender perspective. An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

Technical Competences

Especifics

  • CE15 - To acquire, formalize and represent human knowledge in a computable form for solving problems through a computer system in any field of application, particularly those related to aspects of computing, perception and performance in intelligent environments or environments.
  • CE17 - To develop and evaluate interactive systems and presentation of complex information and its application to solving human-computer and human-robot interaction design problems.
  • CE24 - To ideate, design and build intelligent robotic systems to be applied in production and service environments, and that have to be capable of interacting with people. Also, to create collaborative and social intelligent robotic systems.
  • CE25 - To ideate, design and integrate mobile robots with autonomous navigation capability, fleet formation and interaction with humans.
  • CE28 - To plan, ideate, deploy and direct projects, services and systems in the field of artificial intelligence, leading its implementation and continuous improvement and assessing its economic and social impact.

Generic Technical Competences

Generic

  • CG3 - To define, evaluate and select hardware and software platforms for the development and execution of computer systems, services and applications in the field of artificial intelligence.
  • CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
  • CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.
  • CG6 - To identify opportunities for innovative applications of artificial intelligence and robotics in constantly evolving technological environments.
  • CG7 - To interpret and apply current legislation, as well as specifications, regulations and standards in the field of artificial intelligence.
  • CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
  • CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

Objectives

  1. To know robot components and what's the difference againts other authomatic machines
    Related competences: CG3, CG5, CT2,
  2. To know the different types of robots that are in the market and their characteristics. Understand their manuals and specifications, as well as regulations and standards according to current legislation.
    Related competences: CT2, CG3, CG5, CG7,
  3. To know the different sources of sensory information and their characteristics.
    Related competences: CG3, CG5,
  4. To be able to merge different sources of information to obtain, formalize and represent the physical environment in a computable way for problem solving.
    Related competences: CT2, CE15, CG4, CG5, CG6, CG8,
  5. To learn how to program robots and design robotic applications.
    Related competences: CT1, CT2, CE15, CE17, CE24, CE25, CG4, CG6, CG8, CG9,
  6. To learn how to coordinate actions between robots.
    Related competences: CT1, CE15, CE24, CE25, CG4, CG6, CG9,
  7. To be able to make judgments that include a reflection on relevant issues of a social, scientific or ethical nature, related to current robotics and its potential applications.
    Related competences: CT8, CE28, CG5, CG6, CT2, CG7, CG8,

Contents

  1. Introduction
    Robots and Robotics. Evolution of robots. Incidence of robotics in today's society.
  2. Robot morphology.
    Components. Structures and characteristics of robots.
  3. Mobile robots
    Mechanisms of locomotion. Types of mobile robots. Direct and inverse kinematics. Maneuverability.
  4. Perception of the environment
    Sensor classification. Characteristics. Depth sensors. Orientation sensors.
  5. Mobile robot navigation
    Reactive navigation. Obstacle escape. Map-based planning.
  6. Location of mobile robots
    Location systems (GPS, US, IR, fixed routes). Navigation based on reference points.
  7. Manipulator robots
    Architectures and features
  8. Cinemática de los robots manipuladores.
    Geometric transformations. Direct and Reverse Kinematics. Redundancy. Singularities.
  9. Generation of trajectories
    Paths and trajectories. Trajectories in the joint space. Trajectories in Cartesian space.
  10. Robot Programming and Control
    Joint space control. Manipulator control architecture. Robot programming environments and languages.
  11. Applications of robotics
    Industrial Robotics. Service robotics. Exploration robotics. Medical and healthcare robotics.

Activities

Activity Evaluation act


¿Qué es un robot?

Robots and Robotics. Evolution of robots. Incidence of robotics in today's society.
Objectives: 1 7
Contents:
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Robot morphology.

Components. Structures and characteristics of robots.
Objectives: 1 2
Contents:
Theory
4h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
4h

Mobile robots

Mechanisms of locomotion. Types of mobile robots. Direct and inverse kinematics. Maneuverability.
Objectives: 2 5
Contents:
Theory
4h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
8h

Perception of the environment

Sensor classification. Characteristics. Depth sensors. Orientation sensors.
Objectives: 3 4
Contents:
Theory
2h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
8h

Mobile robot navigation

Reactive navigation. Obstacle escape. Map-based planning.
Objectives: 4 5
Contents:
Theory
4h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
8h

Location of the mobile robot

Location systems (GPS, US, IR, fixed routes). Navigation based on reference points
Objectives: 4 5 6
Contents:
Theory
2h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
8h

Manipulator robots

Architectures and features
Objectives: 2 3
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
4h

Kinematics of manipulating robots

Geometric transformations. Direct and Reverse Kinematics. Redundancy. Singularities. Generation of trajectories
Objectives: 2 5
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
4h

Generation of trajectories

Paths and trajectories. Trajectories in the joint space. Trajectories in Cartesian space.
Objectives: 5 6
Contents:
Theory
2h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
4h

Robot Programming and Control

Joint space control. Manipulator control architecture. Robot programming environments and languages
Objectives: 4 5 6
Contents:
Theory
4h
Problems
0h
Laboratory
6h
Guided learning
0h
Autonomous learning
12h

Aplicaciones de la robótica

Industrial Robotics. Service robotics. Exploration robotics. Medical and healthcare robotics
Objectives: 5 7
Contents:
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Exercise resolution

Resolution of evaluable exercises (between 3 and 6) carried out as personal work or in pairs
Objectives: 1 2 3 4 5 6 7
Week: 1 (Outside class hours)
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h

Teaching methodology

The teaching methodology will be generally of a deductive nature. Attempts will be made to avoid the expository method / Master class. The approach will always be the same:
¿ Propose a problem
¿ try to solve it
¿ add the necessary pieces of theory to be able to solve the problem properly.

No distinction will be made between theory and problem classes, as the presentation of concepts and the solution of application problems are interspersed in the classroom sessions. Laboratory classes are the complement where students put the concepts into practice with the use of simulators and / or real robotic systems.

In addition to the activities in the classroom and in the laboratory, students must solve and deliver to the teachers for their evaluation a set of exercises, which allow to consolidate the acquired knowledge, be a mechanism of self-evaluation and work in equipment.

Evaluation methodology

The course will be evaluated continuously. There will be no final exam.
Throughout the course, a series of exercises will be requested that will help the teacher evaluate the student at the end of the course. These exercises can be both face-to-face and non-face-to-face and can consist of the presentation of the results of practices developed in the laboratory (NL), as well as the theoretical / practical solution of problems proposed by the teacher in class (NT).

The final grade of the course will be calculated as follows: NF = 0'6NL + 0'4NT

Bibliography

Basic:

Previous capacities

Mathematics

* To know and be able to apply the concept of derivative and partial derivative.
* To know the basic methods of graphical representation of functions (asymptotes, maxima, minima, ...).
* To know the elementary properties of trigonometric functions.
* To know the basic concepts of manipulation and operation with matrices.

Programming and Data Structure

* To know how to specify, design and implement simple algorithms with an imperative programming language.
* To know how to build correct, efficient and structured programs.
* To know the concepts of interpreted languages and compiled languages.
* To know search algorithms on data structures (tables, lists, trees, ...).

Computer Architecture and Technology

* To know at a functional level the different types of logic gates.
* To know how to analyze and implement simple combinational and sequential logic systems.
* To know the basic structure of a computer.
* To know the input / output and interruption subsystem of computers.