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Design of Autonomous Systems

What Will Learn?

  • Course Aims
    The aim of this course is the following: • Introducing the students to the field of studying and developing autonomous systems including their main challenges such as navigation, localization, mapping and control architectures and their related analysis. • Investigate the different path-planning algorithms and their relative advantages and disadvantages in terms of the completeness, optimality, feasibility and computational cost in the development of different autonomous systems. • Introduce the reasons for sensors’ uncertainty found in the implementation of autonomous systems and the usage of different state estimators on the prediction of different states. • Introduce the concept of control architectures used in autonomous systems, including concepts of control discretization usually used in embedded systems as well as the concepts of intelligent controllers used usually in high-level controllers.
  • Course Goals
  • Quality Education
  • Industry, Innovation and Infrastructure

Requirements

MCT342

Description

  • English Description

    Introduction to autonomous systems: autonomous versus automatic systems, automated and autonomous human-centered technical systems, semi-autonomy, autonomous behavior. Perception: multi-sensor fusion, localization, navigation and mapping, obstacle recognition and detection. Planning and actuation: task decomposition, reactive behavior, pre-planned knowledge and skill-based behavior. Knowledge-base: facts and procedures, acquisition, exploration, skill transfer, learning. Autonomous systems architecture: behavioral principles, expert systems, knowledge-bases, multi-level control concepts. Applications of autonomous systems
  • Arabic Description

    Introduction to autonomous systems: autonomous versus automatic systems, automated and autonomous human-centered technical systems, semi-autonomy, autonomous behavior. Perception: multi-sensor fusion, localization, navigation and mapping, obstacle recognition and detection. Planning and actuation: task decomposition, reactive behavior, pre-planned knowledge and skill-based behavior. Knowledge-base: facts and procedures, acquisition, exploration, skill transfer, learning. Autonomous systems architecture: behavioral principles, expert systems, knowledge-bases, multi-level control concepts. Applications of autonomous systems
  • Department
    Mechatronics Engineering
  • Credit Hours
    3
  • Grades
    Total ( 100 ) = Midterm (20) + tr.Major Assessment (15 = tr.Industry 0% , tr.Project 14% , tr.Self_learning 2% , tr.Seminar 4% ) + tr.Minor Assessment (5) + tr.Oral/Practical (20) + Exam Grade (40)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 2, Lab Hours: 1
  • Required SWL
    100
  • Equivalent ECTS
    4
  • The following list of references are used in this course as helpful material:
  • - Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots. MIT press.
  • - Course Lectures available on LMS
  • - Tutorials available on LMS
  • - ROS-Wiki (http://wiki.ros.org/Documentation)
  • - Legal Issues of Driver Assistance Systems and Autonomous Driving,( https://08102rxay-1103-y-https-link-springer-com.mplbci.ekb.eg/referenceworkentry/10.1007/978-0-85729-085-4_58 ) EKB