Advanced Robotics

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
ESAII
The course "Robòtica Avançada" is a high-level subject that allows students to acquire specialized knowledge about the programming, control and operation of manipulator and mobile robots. Through this course, students will be able to deepen their skills of perception, planning and execution, as well as the ability to solve problems in real and unpredictable environments.

The students will learn to apply advanced techniques of artificial intelligence to solve robotics problems in complex and dynamic environments. This includes planning tasks, movements and routes, reasoning about tasks to be performed, space, managing uncertainty, accommodation between objects, perception and other advanced skills.

Teachers

Person in charge

  • Isiah Zaplana Agut (isiah.zaplana@upc.edu)

Others

  • Anais Garrell Zulueta ( anais.garrell@upc.edu)

Weekly hours

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

Competences

Transversal Competences

Transversals

  • CT1 - 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 - 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.
  • CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
  • CT5 - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
  • 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.

    • Basic

  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

    • 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.
  • CE26 - To design and apply techniques for processing and analyzing images and computer vision techniques in the area of artificial intelligence and robotics
  • 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. Learn to program robots and design robotic applications.
      Related competences: CB5, CT1, CT5, CG4, CE15, CE17, CE24, CE25, CE26, CB3, CG6, CG8, CG9,
    2. Be able to make judgments that include a reflection on relevant social, scientific or ethical issues related to current robotics and its potential applications.
      Related competences: CT2, CT3, CT8, CE28, CG5, CG6, CG7, CG8,
    3. Learn to coordinate actions between robots.
      Related competences: CT3, CT5, CE15, CE17, CG3, CG5, CG6,
    4. 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: CB3, CB5, CT2, CT5, CE17, CE24, CE25, CG3, CG5, CG6, CG8,
    5. Application of Computer Vision techniques to Robotic Systems
      Related competences: CT5, CE15, CE24, CE25, CE26, CG4, CG5, CG6,
    6. Application of Artificial Intelligence techniques to Robotic Systems
      Related competences: CT3, CT5, CE15, CE17, CE24, CE26, CE28, CG4, CG5, CG9,
    7. Creation of interaction systems between robots and humans
      Related competences: CT3, CT5, CT8, CE15, CE17, CT1, CE24, CE25, CE26, CE28, CG3, CG4, CG5, CG6, CG7, CG8, CG9,

    Contents

    1. Introduction
      The contents that were achieved in the previous subject Introduction to robotics will be reviewed.
    2. Introduction to motion planning -- The configuration space.
      Trajectory planning versus movement planning. Forward and inverse kinematics. Geometric and topological definition of the configuration space of a robotic manipulator.
    3. Potential fields-based solutions to the motion planning problem.
      Discretization of the configuration space of a manipulator robot. Repulsive and attractive potential fields. Functions without local minima: Navigation functions and harmonic functions.
    4. Sampling-based solutions to the motion planning problem.
      Sampling type (random, Halton, SDK, etc.). Methods based on road maps, Probabilistic Road Maps (PRMs), and exploration trees, Randomly Exploring Rapid Trees (RRTs) and their application to movement planning problems. Improvements to standard planners (PRM with Gaussian sampling, RRT-CONNECT, RRT*).
    5. Task planning.
      Motion planning taking into account the restrictions imposed by the tasks. Modelling tasks with directed graphs. STRIPS and PDDLs languages. Search and heuristics guided search algorithms. FF algorithm.
    6. Cognitive robotics.
      Ontologies. Types of ontologies and reasoning from ontologies. Behaviour trees (BTs).
    7. Kinematics of mobile robots.
      Differential kinematics, differential constraints imposed by wheels and the concept of holonomic and non-holonomic robots (review). Differential kinematics, relationship between the velocity of a robotic platform and the differential constraints imposed by a single wheel, forward and inverse differential kinematics for a specific wheeled mobile robot.
    8. Mobile robots' dynamics.
      Dynamics of a mobile robot. Modeling the dynamics.
    9. Perception of mobile robots.
      Types of sensors. Error propagation. Visual Servoing.
    10. Location of mobile robots.
      Introduction to map-based location. Review of probability theory. Markov approach. Kalman filter approach. The problem of SLAM. SLAM-EKF, FastSLAM, GraphSlam.
    11. Route planning for mobile robots.
      Introduction, representations, collision avoidance. Potential fields. Solved example. Graph construction, graph search. Concepts applied to mobile robotics.

    Activities

    Activity Evaluation act


    Review of the contents of the course "Introducció a la Robòtica".

    The contents that were achieved in the previous course "Introducció a la Robòtica" will be reviewed.
    Objectives: 2
    Contents:
    Theory
    1h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    4h

    Introduction to motion planning -- The configuration space.

    Trajectory planning versus movement planning. Forward and inverse kinematics. Geometric and topological definition of the configuration space of a robotic manipulator.
    Objectives: 2 4
    Contents:
    Theory
    1h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Potential fields-based solutions to the motion planning problem.

    Discretization of the configuration space of a manipulator robot. Repulsive and attractive potential fields. Functions without local minima: Navigation functions and harmonic functions.
    Objectives: 2 4
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Sampling-based solutions to the motion planning problem.

    Sampling type (random, Halton, SDK, etc.). Methods based on road maps, Probabilistic Road Maps (PRMs), and exploration trees, Randomly Exploring Rapid Trees (RRTs) and their application to movement planning problems. Improvements to standard planners (PRM with Gaussian sampling, RRT-CONNECT, RRT*).
    Objectives: 2 4
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    12h

    Task planning.

    Motion planning taking into account the restrictions imposed by the tasks. Modelling tasks with directed graphs. STRIPS and PDDLs languages. Search and heuristics guided search algorithms. FF algorithm.
    Objectives: 1 2 3 4 6
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Cognitive robotics.

    Ontologies. Types of ontologies and reasoning from ontologies. Behaviour trees (BTs).
    Objectives: 1 3 5 6 7
    Contents:
    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Kinematics of mobile robots.

    Differential kinematics, differential constraints imposed by wheels and the concept of holonomic and non-holonomic robots (review). Differential kinematics, relationship between the velocity of a robotic platform and the differential constraints imposed by a single wheel, forward and inverse differential kinematics for a specific wheeled mobile robot.
    Objectives: 1 2 3 6
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    10h

    Mobile robots' dynamics.

    Dynamics of a mobile robot. Modeling the dynamics.

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

    Perception of mobile robots.

    Types of sensors. Error propagation. Visual Servoing.

    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    8h

    Location of mobile robots.

    Introduction to map-based location. Review of probability theory. Markov approach. Kalman filter approach. The problem of SLAM. SLAM-EKF, FastSLAM, GraphSlam.

    Theory
    4h
    Problems
    0h
    Laboratory
    4h
    Guided learning
    0h
    Autonomous learning
    12h

    Route planning for mobile robots.

    Introduction, representations, collision avoidance. Potential fields. Solved example. Graph construction, graph search. Concepts applied to mobile robotics.

    Theory
    2h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    10h

    Final Project

    Els alumnes hauran d'aplicar els coneixements obtinguts en un robot real i hauran de fer una demostració del seu funcionament.
    Objectives: 1 2 3 4 5 6
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    - The theoretical classes will be complemented by putting into practice on PC the techniques presented.

    - In the laboratory classes, real computer vision problems will be solved.

    - Problems of higher complexity will be raised as homework.

    Evaluation methodology

    There will be a first midterm exam (P1), worth 35% of the final grade, and a final exam (EF), worth 65%.

    The final grade will be calculated using the following formula:

    NF = 0.35 · NP1 + 0.65 · NEF

    where NP1 is the grade obtained in the first midterm exam and NEF is the grade obtained in the final exam.

    Exceptionally, the final exam may be replaced by a second midterm exam (P2) and the assessment of the final practical assignments (PF). In this case, the first midterm exam will account for 35%, the second midterm exam for 35%, and the final practical assignments for 30% of the final grade.

    This alternative assessment method will only apply if the student satisfactorily meets the following conditions: attending the practical sessions, except in duly justified cases of force majeure; making responsible use of generative artificial intelligence tools; and maintaining an appropriate attitude, as well as proper use of the laboratories and teaching resources.

    In this case, the final grade will be calculated as follows:

    NF = 0.35 · NP1 + 0.35 · NP2 + 0.30 · NPF

    where NP1 is the grade obtained in the first midterm exam, NP2 the grade obtained in the second midterm exam, and NPF the grade obtained in the submitted final practical assignments.

    The application of this alternative assessment method will be subject to satisfactory compliance with all the above requirements.

    Only students who have taken the exams and have not passed them may sit the reassessment exam. The maximum grade that may be obtained in the reassessment is 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 analyse 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.

    Robotics Area

    * Knowledge of ROS.
    * Knowledge of Matlab.
    * Knowledge of the kinematics of both mobile robots and robotic manipulators.