Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    Belépés
    címtáras azonosítással
    vissza a tantárgylistához   nyomtatható verzió    

    Robot Manipulators and Mobile Robots

    A tantárgy neve magyarul / Name of the subject in Hungarian: Robotkarok és mobilis robotok

    Last updated: 2024. február 28.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    MSc in Electrical Engineering

    Robotics Secondary specialization 
    Course ID Semester Assessment Credit Tantárgyfélév
    VIIIMA21   2/1/0/v 5  
    3. Course coordinator and department Gincsainé Dr. Szádeczky-Kardoss Emese,
    Web page of the course https://edu.vik.bme.hu/
    4. Instructors

    Gincsainé Szádeczky-Kardoss, Emese (associate professor, IIT)

    Dr. habil. Harmati, István (associate professor, IIT)

    Dr. Kiss, Bálint (associate professor, IIT)
    5. Required knowledge Mathematics, Control engineering
    7. Objectives, learning outcomes and obtained knowledge The course aims to summarize the theoretical and practical foundations of modeling, control, and implementation of industrial robot systems and unmanned, autonomous mobile robots. The course presents the most common types of robots, the theoretical basis of their modeling, and the methods of their trajectory planning and control. It gives details about the modern sensors used for the navigation of mobile robots and the basic sensor fusion solutions, as well as different motion planning methods. Students who successfully pass the course can participate in assembling complex robot systems and developing and implementing their control algorithms.
    8. Synopsis

    Lectures:

    Basics of Mechatronics

    Repetition and introduction of mathematical relationships and notations. Description of the position and orientation of rigid bodies in the 2D plane and 3D space.

         Kinematic models of robotic manipulators

    Denavit-Hartenberg form of robotic manipulators. Direct and inverse geometric problem. Jacobi matrix of robots. Redundant and underactuated cases.

         Dynamic models of robotic manipulators

    Lagrange equation. Dynamic model based on the Lagrange equation. Example with a 2-DoF robot arm.

         Control of robotic manipulators

    Decentralized 3-loop cascade control. Computed torques method. Hybrid position and force control.

         Trajectory planning of robotic manipulators

    Path planning task illustrated by the pin-hole problem. Polynomial trajectory planning in joint variables.

         Introduction to mobile robotics

    Types of mobile robots, mathematical models (wheeled, legged, and flying robots).

         Navigation of mobile robots

    Presentation of navigation methods. Sensors of the inertial navigation; measured and calculated quantities. Sensing the environment.

         Sensor fusion

    LS estimation, Kalman filter, Particle filter, formulation of SLAM problem.

         Hierarchical implementation of motion planning

    Global and local planning methods. Types of maps and applicable planning solutions (deterministic and probabilistic), reactive planners (e.g. APF, Bug, VO methods).

         Optimal path planning for a mobile robot in the plane

    The optimal path for Dubins and Reeds-Shepp type robots. Continuous curvature path planning.

         Coverage path planning

    Formulation of coverage planning problems, areas of application, and solutions (random, systematic coverage methods).

         Path tracking control of mobile robots

    Control based on error transformation, flatness-based control, PI type solutions.

         Robot Operating System

    Basic of ROS architeckture: ROS 1 and ROS 2 shortly.

    Practices:

    - Modelling a robot manipulator in Matlab-Simulink-Simscape environment (direct and inverse geometry)

         - Control of a robot in Matlab-Simulink-Simscape environment 

         - Odometry of a differential driven mobile robot

         - Inertial navigation (components of IMU and their fusion, advantages, and disadvantages)

         - Using Kalman filter for navigation

         - Determining the optimal path for Dubins type robots

         - Tracking control of a mobile robot

    9. Method of instruction Two lectures in a week, and two practices in every two weeks. Concepts and methods based on each other are presented during the semester, so thorough and continuous preparation is recommended to understand the material of the lectures and practices. The Matlab-based development environment used in the practices is available to students on virtual machines during and outside of the practices.
    10. Assessment

    Study period:

    There are two requirements to get a signature. Students have to satisfy both:

    1. Homework: Successful submission of the solution to the individual homework assigned to each student during the semester. (Evaluation: accepted/not accepted).

    2. Mid-term exam: One 90-minute-long mid-term exam is written during the semester. Its result must be at least 2 (on a scale of 1 to 5). The topics of the mid-term exam contain half of the entire semester’s topics. The result of the mid-term gives 40 percent in the result of the final exam.

    Exam period:

     Obtaining a signature is a condition for admission to the exam. The exam consists of a written test and the inclusion of the result achieved for the mid-term exam. (There is no possibility to improve the result of the mid-term exam during the exam period.)

    11. Recaps The mid-term exam can be repeated (or its result can be improved) once in the teaching period. The mid-term exam cannot be repeated in the retake period. Late submission of the homework is possible in the retake period.
    12. Consultations Before or after the lectures and practices. One day before the exams if required. 
    13. References, textbooks and resources

    Lecture slides and additional materials on the website of the course. 

    LaValle, S. M.: Planning Algorithms. Cambridge University Press, 2006, ISBN-13: 978-0521862059

    14. Required learning hours and assignment
    Contact hours42
    Preparation hours during the study period28
    Preparation for mid-term15
    Preparation of homework30
    Learning assigned written curriculum20
    Preparation for examination15
    Altogether [hours}150
    15. Syllabus prepared by
    Gincsainé Szádeczky-Kardoss, Emese (associate professor, IIT)
    Dr. habil. Harmati, István (associate professor, IIT)
    Dr. Kiss, Bálint (associate professor, IIT)