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
Robot Platform: TURTLEBOT 4
Other Hardware: Nvidia Jetson Nano & Raspberry Pi-4
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
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