Reference docs, a ROS2 command cheat sheet, and answers to the questions we hear most from new learners.
Official documentation for installation, tutorials, and API references used throughout modules 3–12.
Deep reference for costmaps, planner/controller plugins, and behavior trees covered in modules 7–8.
Computer vision library used for image processing and pre/post-processing around object detectors.
Simulate your robot's URDF, sensors, and physics before ever touching real hardware.
Deep learning framework used for training and fine-tuning the object detection models in Module 6.
Recommended editor — pair it with the ROS2 and Python extensions for linting and debugging.
Prices in the Bill of Materials assume typical hobbyist-electronics retail pricing in India. Compare a few sellers before buying — LiDAR and battery prices in particular vary a lot.
Often the cheapest source for chassis kits, motors, drivers, and passive components — and you can inspect quality before buying.
Specialty Indian robotics/electronics stores stock RPLidar units, IMUs, and Raspberry Pi boards with local warranty support.
A spare Raspberry Pi, an old webcam, or a salvaged gear motor can shave a meaningful chunk off your total build cost.
| Command | What it does |
|---|---|
ros2 node list | List all currently running nodes |
ros2 topic list | List all active topics |
ros2 topic echo /scan | Print live messages published on a topic |
ros2 topic pub /cmd_vel geometry_msgs/Twist "{...}" | Manually publish a velocity command |
ros2 run <pkg> <executable> | Run a single node from a package |
ros2 launch <pkg> <file>.launch.py | Launch a full set of nodes together |
ros2 bag record -a | Record all topics for later playback/debugging |
colcon build --symlink-install | Build your workspace packages |
ros2 doctor | Sanity-check your ROS2 environment setup |
No. You need basic comfort with Python and the command line. Modules 1–2 bring everyone up to speed on Linux, Git, and the math refresher before ROS2 begins in Module 3.
Yes — every concept has a browser-based interactive lab, and Gazebo simulation covers the same ROS2/Nav2 workflow. The DIY build (Modules 11–12) is where you apply it to real hardware, but it's not required to learn the material.
ROS2 Humble Hawksbill (LTS) on Ubuntu 22.04 — it's what all course examples, launch files, and the DIY build guide target.
Skip the RPLidar and go camera + ultrasonic-only for roughly ₹20,600 — see the callout on the Bill of Materials section. You'll still cover differential drive, ROS2, and camera-based object detection; you can add LiDAR-based SLAM later.
No — Module 6 focuses on lightweight, edge-optimized detectors (MobileNet-SSD, small YOLO variants) that run in real time on a Raspberry Pi, optionally accelerated with a Coral USB TPU.
A Raspberry Pi 4 (4GB) is the reference board for pricing and instructions, but any board that runs Ubuntu 22.04 and has enough GPIO/USB for your sensors (a Pi 5, a Jetson Nano, or similar SBC) will work with minor adjustments.
The course roadmap and setup instructions in the first two modules cover tooling, environment setup, and what to expect.