INSTRUCTOR

    Md Jahidul Islam. Office Hours: Friday 4:00 PM - 5:00 PM. @LAR-339D.

CLASS SCHEDULE

    Lecture: M/W/F 3:00PM-3:50PM @LAR-330

TAs

    TBD.

COURSE PREREQUISITES

    Microprocessor Applications or embedded systems or equivalent courses
    Fluent in object-oriented programming (Python and/or C++)
    Basics of linear algebra and calculus

Textbooks

    Introduction to Robotics: Mechanics and Control (Pearson; 4th Edition).
    By John Craig. ISBN-13: 978-0133489798. Pearson; 4th edition.

    Probabilistic Robotics (Int. Robotics & Autonomous Agents series; 1st Edition).
    By Sebastian Thrun, Wolfram Burgard and Dieter Fox.
    ISBN-13: 978-0262201629, ISBN-10: 0262201623.

RECOMMENDED MATERIALS

Lecture Topics

  • Course Introduction and logistics
      Robotics and AI overview: past, present, and future
      ROS overview: ROS/ROS2 for robotics
  • Robot Systems Components and Sensory Integration
      Electronics and computational platforms
      Sensory integration and design choices
      User interface, Integration, Middleware (ROS)
  • Locomotion: ground robots (UGVs)
      2-DOF and 3-DOF robots
      Forward and inverse kinematic
      Wheel types and arrangements
  • Locomotion: underwater (AUVs/ROVs) and aerial robots (UAVs)
      5-DOF and 6-DOF robots
      Dynamics of thrusters and propellers
      Advanced topics: SOTA locomotion systems
  • Perception: UGVs / AUVs / UAVs
      2D and 2D perception
      Visual and inertial measurements
      Acoustic and hyperspectral perception
  • Deep diving into visual perception
      Robot vision basics - UGVs / AUVs / UAVs
      Image processing and filtering
      Object detection, tracking, and following
      Stereo vision and 3D perception
      Advanced topics: scene segmentation and geometry
  • Planning: motion planners and path planners
      Dynamic programming and SOTA planners
      UGV and UAV planning: social and behavioral aspects
      AUV planning: semi-autonomous and long-term mission planning
  • SLAM: Simultaneous Localization and Mapping
      2-DOF, 3-DOF, and 6-DOF robots
      Advanced topics: VInS (Visual inertial SLAM)
      Acoustic and optical localization
  • Control: linear and non-linear controls
      Kalman Filtering (KF), extended KF, unscented KF
      Advanced topics: particle filters and probabilistic filtering
      Summary discussions: UGVs / AUVs / UAVs

Homework Assignments

  • Hands-on Homework 1: ROS integration and simulation
      Part A: ROS/ROS2 installation and setup
      Part B: Topics and message passing, services
      Part C: Robot bringup and tele-operation
      Part D: Obstacle avoidance is ROS
  • Analytical Homework 1: Kinematics
      Part A: Spatial descriptions and transformations
      Part B: Forward kinematics of 2-DOF and 3-DOF robots
      Part C: Inverse kinematics of 2-DOF and 3-DOF robots
      Part D: Jacobians of velocity and static forces
  • Hands-on Homework 2: Kinematics
      Part A: RViz setup and world mapping
      Part B: Dynamics of a wheeled robot (TurtleSim)
      Part C: Gaze control of a wheeled robot
      Part D: Dynamics of a underwater robot (RViz)
  • Hands-on Homework 3: Visual Perception
      Part A: Object detection and tracking
      Part B: Human/object following by robots
      Part C: Low-light sensing or hyper-spectral perception
  • Hands-on Homework 4: Active Planner
      Part A: Dynamic programming with a basic planner
      Part B: Active planner for a maze solver robot
      Part C: Planning to see better
  • Analytical Homework 2: Active Planner
      Part A: Basic estimation theory (likelihood and posterior estimators)
      Part B: 2D and 3D localization from known landmarks
      Part C: Kalman filtering for engineers
      Part D: Probabilistic (Bayesian) filtering exercise