Aurora Robotics Core Workshop 2: Autonomous Vehicles

01 Robots for this Workshop

We are thrilled to introduce two newly acquired, state-of-the-art autonomous vehicles that will serve as the core teaching platforms for this workshop. These robots will provide you with diverse challenges in kinematics, control, and perception.

The ROSMASTER A1

This high-performance, four-wheeled platform is designed for agile indoor navigation and precise maneuvering.

Cutting-Edge Sensors: Equipped with a top-mounted 3D LiDAR for 360-degree environmental mapping and a front-facing stereo depth camera for accurate object detection and distance sensing.
Modern Design: Features a sleek, dark grey chassis with a powerful onboard compute unit, making it an ideal platform for processing complex ROS 2 algorithms.
Workshop Role: You will use the A1 to master Ackermann steering kinematics, implement SLAM (Simultaneous Localization and Mapping), and develop sophisticated path-planning algorithms for complex indoor environments.

The Autonomous Tracked Robot

Our new tracked vehicle offers a robust alternative, presenting different control and navigation challenges suited for more varied terrain.

Robust Mobility: The tank-like tread drive system provides superior traction and stability, allowing for exploration of different driving dynamics compared to wheeled platforms.
Active Perception: Features a stereo camera mounted on a pan-tilt mechanism, enabling dynamic visual tracking and a wider field of view for environmental awareness, complemented by its own LiDAR sensor.
Workshop Role: This robot will be used to teach differential drive control, explore skid-steer kinematics, and implement active vision algorithms where the robot must move its camera to find and track targets.

Your Remote Lab Access

Experience true Hands-On Learning from anywhere in the world. Through our secure remote lab infrastructure, you will move beyond simulation and deploy your code directly onto these physical robots located in the Aurora lab.

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Deploy & Execute

You will SSH directly into the robots to run your ROS 2 nodes and Python scripts.

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Live Feedback

Watch your algorithms bring the robots to life through real-time camera feeds.

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Real-Time Visualization

Visualize sensor data from the LiDAR and cameras instantly on your own machine using tools like RViz, allowing you to debug and optimize your code as if you were in the lab with the robot.

This unique setup bridges the critical gap between theory and reality, ensuring you gain the practical skills needed to build real-world autonomous systems.

02 About the Workshop

Experience the future of robotics through our intensive 8-week hands-on workshop. Using autonomous vehicles as our primary teaching platform, you'll dive deep into the world of robotics, AI, and autonomous systems.

This cohort specifically focuses on the autonomous vehicles, moving from concepts and ideas to foundational mathematical concepts and "no-framework" programming to advanced deployment within the ROS 2 ecosystem. You won't just study theory; you will build, simulate, and witness your code controlling a physical machine in real-time.

The Core Pillars

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Hands-On Learning

Build and program real autonomous vehicles from scratch.

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The Math Behind the Machine

Master the rigid body transformations, coordinate frames, and PID control theory that drive professional robotics.

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AI & Machine Learning

Implement computer vision, sensor fusion, and decision-making algorithms.

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Cutting-Edge Tech

Work with the latest robotics hardware and software frameworks, including ROS 2, Gazebo, and RViz.

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Real-World Projects

Complete projects that solve actual autonomous navigation challenges.

How We Learn

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Bi-Weekly Deep Dives

Two intensive classes per week covering the "What," "Why," and "How" of autonomous systems.

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Live Hardware Sessions

Watch your code come to life during live runs on the Yahboom ROSMASTER A1 in our Aurora lab via online live sessions.

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Weekend Bridge Sessions

Dedicated support for those new to programming to ensure no one is left behind.

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Continuous Practice

Daily engagement and coding challenges within our community group chat to keep your momentum high.

03 8-Week Curriculum

Week 1: The Landscape of Autonomy & Git Foundations

Introduction to Robotics & AVs: Defining autonomy levels 1-5 and the Perception-Planning-Control pipeline.
Problems in AV Robotics: Addressing Localization, Mapping, and Path Planning challenges in real-world environments.
Git & GitHub Integration: Mastering industry-standard version control and collaborative workflows for robotics.
Hardware Setup: Initializing the Yahboom ROSMASTER A1 and established computing environments.

Week 2: The Mathematics of Motion

Kinematics & Coordinate Frames: Linear/Angular velocity, Rotation Matrices, and Ackermann Steering geometry.
Control Theory Foundations: Understanding open-loop vs. closed-loop control systems.
PID Control: Comprehensive math behind Proportional, Integral, and Derivative controllers for smooth movement.

Week 3: Coding the Logic (Pure Python/C++)

Solving AV Problems without ROS 2: Writing raw obstacle avoidance and simulating virtual robots with math libraries.
Live Session 1: SSH into the ROSMASTER A1 to execute "No-ROS" scripts and observe hardware reactions.
Performance Observation: Analyzing where raw code fails and why a framework like ROS 2 is necessary.

Week 4: Transitioning to the ROS 2 Architecture

Why ROS 2?: Deep dive into Nodes, Topics, Messages, Services, and the DDS Layer.
Workspace Management: Creating packages, managing Colcon builds, and source/overlay concepts.
Initial Deployment: Running the first official ROS 2 nodes on the physical hardware.

Week 5: Digital Twins (URDF & RViz)

Building the A1 in URDF: Using Xacro to define the chassis, wheels, and sensor placements (LiDAR/Camera).
TF Transform Tree: Establishing the critical relationship between base_link and laser_frame.
Visualization in RViz 2: Real-time debugging of LaserScan and Odometry sensor data.

Week 6: Simulation in Gazebo

Physics Integration: Adding inertia, friction, and mass properties to the URDF model.
Gazebo Plugins: Implementing differential drive and LiDAR sensors within the virtual environment.
Sim-to-Real Calibration: Tuning controllers in simulation to prepare for physical deployment.

Week 7: Sensing, Perception & Live Integration

LiDAR & Vision Basics: Processing LaserScan for 2D safety and an introduction to OpenCV on the A1.
Live Session 2: Running Gazebo-tested ROS 2 nodes on the physical robot in the Aurora lab.
Autonomous Navigation: Integrating mapping and control for self-guided vehicle behavior.

Week 8: The Capstone – Autonomous Navigation

Intro to Nav2: Understanding the Navigation Stack, SLAM, and Path Planning costmaps.
Final Live Showcase: Presenting complete autonomous logic running on the ROSMASTER A1.
Certification: Course wrap-up, final project reviews, and career pathway guidance.

Extra Support Streams

The Weekend "Bridge": Dedicated Saturday/Sunday sessions for Python Essentials and Linux Terminal mastery.
Group Chat Practice: Daily Mon/Wed/Fri Code Challenges and 24/7 Mentor Q&A for debugging build errors.

AURORA ROBOTICS CORE WORKSHOP 2: AUTONOMOUS VEHICLES