AEP 2026: Mobile Robotics Class 1

This video serves as an introductory session for a mobile robotics course, outlining the program's scope and foundational concepts. Program Overview & Expectations: The course aims to move beyond basic tutorials, focusing on building practical, industry-relevant skills. Over the coming weeks, the curriculum will cover Linux, version control, control theory, embedded systems, ROS 2, Gazebo, and various wheeled drive systems (such as differential and Ackermann steering). Participants are encouraged to be "shameless learners"—actively asking questions and engaging in independent research to grasp complex topics intricately. Defining Mobile Robotics: Mobile robotics is defined as a field of engineering focused on the design and production of robots capable of locomotion, meaning they can move their entire base from one location to another. This is contrasted with stationary robots, such as robotic arms, which may move joints but remain fixed in place. Mobile robots are often classified by their operating medium: aerial (UAVs/drones), ground (UGVs), and underwater (AUVs/ROVs). Mathematical Foundations: The course emphasizes a conceptual understanding of mathematics, as these tools are essential for robotics engineering. Derivatives and Integrals: These are explained in the context of sensor data and motion, where higher sampling frequencies (shorter intervals) lead to more accurate acceleration estimates and distance calculations. Linear Algebra: Vectors, which bundle magnitude and direction, are used to represent physical properties like velocity and force. Matrices are utilized for more complex data arrangements and transformations. Orientations: The session introduced Euler angles (roll, pitch, and yaw), rotation matrices, and quaternions. Quaternions are highlighted as the industry standard for calculations because they are computationally faster and avoid "gimbal lock". Coordinate Frames: The importance of distinguishing between a fixed "global" or "world" frame and a dynamic "robot" frame is discussed. Understanding the relationship between these frames is critical for localization—determining where the robot and sensed objects are located within an environment.