Introduction to Robotics

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

Others

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: CG3, CG5, CG7, CT2,
    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: CG4, CG5, CG6, CG8, CT2, CE15,
    5. To learn how to coordinate actions between robots.
      Related competences: CE24, CE25, CG4, CG6, CG9, CT1, CE15,
    6. 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: CE28, CG5, CG6, CG7, CG8, CT2, CT8,
    7. To learn how to program robots and design robotic applications.
      Related competences: CE24, CE25, CG4, CG6, CG8, CG9, CT1, CT2, CE15, CE17,

    Contents

    1. Introduction
      Robotic history, types of robots and robo-ethics
    2. Perception
      Uncertainty, sensor perception, noise position and normal distribution
    3. Localization I
      Probabilidad condicional y teorema de bayas, localización, filtro bayesiano, filtro de kalman
    4. Localization II
      Ejemplo FIltro de Kalman, filtro de kalman extendido, filtro de partículas
    5. Mapping
      Mapas, Slam, movimiento, ruedas
    6. Forward kinematics, inverse kinematics
      Calculation of direct and indirect kinematics, example of vehicles with wheels
    7. Planning
      Planning, exploring, borders, replanning, exploring vs. exploitation
    8. Introduction to manipulator robots.
      Definition of a manipulator robot. Types and components. Position and orientation of a rigid solid. Representations of orientations.
    9. Direct kinematics of the manipulator robot.
      Reference systems and coordinate systems. Joint coordinate systems. DH parameters and homogeneous transformation matrices. Direct kinematics.
    10. Differential kinematics of the manipulator robot.
      Linear and angular velocity of a rigid solid. Velocity propagation. Geometric and analytical Jacobian of a robot manipulator. Singularities of a robot.
    11. Inverse kinematics of the manipulator robot I
      Analytical inverse kinematics of simple robots. Pieper's theorem and method for analytical inverse kinematics of manipulator robots with spherical wrist. Numerical methods based on the Jacobian matrix for the inverse kinematics of a manipulator robot (pseudoinverse, transposed, etc.).
    12. Inverse kinematics of the manipulator robot II
      Numerical methods based on the Jacobian matrix for the inverse kinematics of a manipulator robot (pseudoinverse, transposed, etc.).

    Activities

    Activity Evaluation act


    Introduction

    Història robòtica, tipus de robots i robo-ètica
    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 7
    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
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Mobile robot navigation

    Reactive navigation. Obstacle escape. Map-based planning.
    Objectives: 4 7
    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 7 5
    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 7
    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: 7 5
    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 7 5
    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: 7 6
    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 7 5 6
    Week: 1 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    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

    There will be two partial tests P1 and P2 with marks NP1 and NP2. There is no final exam.

    There will be a minimum of one evaluable exercise presented in the theoretical class with an E grade.

    There will be a final practice with an NPF grade.

    The final grade of the subject will be calculated as follows: NF=0'3·NP1+0.3·NP2+0'1·E+0.3·NPF

    Attendance at laboratory classes is mandatory, justified non-attendance will penalize the final grade of the subject.

    Solo se podrán presentar a la reevaluación esas persona que, habiéndose presentado a los exámenes parciales los hayan suspendido.La nota máxima que se podrá obtener es un 7.

    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.