ROB521 explores the fundamentals of mobile robotics and sensor-based perception. It's designed for applications in diverse environments such as space exploration, search and rescue, and autonomous vehicles. The course covers key areas including sensors, state estimation, computer vision, localization, mapping, and path tracking. Students engage in hands-on labs using simulations and hardware kits to apply theoretical concepts.

1. Understand the evolution and current state of robotics.
2. Learn classic algorithms for mobile robot autonomy.
3. View mobile robotics as complex systems with integrated components.
4. Gain practical experience through laboratory exercises with real mobile robots.

Students should have foundational knowledge in kinematics, dynamics, control theory, probability theory, and linear algebra. Familiarity with ROS, Virtualbox or Docker is required for labs, and MATLAB is needed for assignments.

The course is divided into modules that cover the essential aspects of mobile robotics and perception:

• Introduction and History of Mobile Robots
• Sensors and State Estimation
• Computer Vision and Control Architectures
• Localization and Mapping
• Path Planning and Tracking
• Software Frameworks for Robotics

These topics are interwoven with practical examples and real-world applications, reinforcing the theoretical knowledge with hands-on labs.

• Main Textbook: "Introduction to Autonomous Mobile Robots" (2nd Ed.) by Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza
• Additional Texts:
  • "Probabilistic Robotics" by Sebastian Thrun, Wolfram Burgard, and Dieter Fox
  • "Planning Algorithms" by Steven LaValle
• Online Courses:
  • Autonomous Mobile Robots on EDX
  • Self-Driving Cars Specialization on Coursera

These resources provide a comprehensive foundation and supplementary knowledge that supports the course lectures and labs.

Labs offer hands-on experience with the TurtleBot 3 system, using the Robot Operating System and Gazebo simulator. Students will perform tasks in teams, learning to apply classroom theories to real-world robotics applications.

• Mondays, 3:00-6:00 PM EST (PRA0101/0102)
• Fridays, 3:00-6:00 PM EST (PRA0103/0104)

• Lab 1: Turtlebot Familiarization and Control
• Lab 2: Path Planning with RRT
• Lab 3: Lidar Mapping and State Estimation
• Lab 4: Lidar SLAM Implementation

Labs focus on progressively building skills from basic robot control to complex tasks like SLAM. Lab 1 is optional but essential for mastering the software used in subsequent labs. Only Labs 2, 3, and 4 require formal reports.