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Autonomous Mobile Robot: RRT*, ROS & Navigation
Rating: 2.0 out of 5(1 rating)
3 students

Autonomous Mobile Robot: RRT*, ROS & Navigation

A complete hands-on guide to robot navigation: sensors, motion control, mapping, and system integration.
Created byYifei zhang
Last updated 12/2025
English

What you'll learn

  • Implement the RRT and RRT* algorithms from scratch, understand their mathematical foundations, and apply them to real-world mobile robot navigation.
  • Build a complete motion-control pipeline including angle alignment, encoder-based distance control, PID tuning, and closed-loop navigation.
  • Integrate Python, ROS, Arduino, and IMU/encoder sensors to create a fully functional autonomous ground vehicle system.
  • Design and execute multi-waypoint path planning tasks, enabling a robot to navigate through complex environments with high accuracy.

Course content

7 sections • 13 lectures • 30m total length

  • Project Demo0:59

    In this lesson, you will see the complete working demo of the robot system you will build in this course.
    We start by showcasing the final result — a fully autonomous indoor robot capable of:

    • Planning paths using RRT*

    • Aligning orientation with IMU (BNO055) feedback

    • Driving precise distances using wheel encoders

    • Executing multi-waypoint navigation automatically

    • Communicating between a PC (ROS) and two Arduino controllers

    You will see the robot rotate to the correct heading, move along the planned path, and complete each navigation segment using closed-loop control.

    This demo gives you a clear picture of the system you are going to build step-by-step in the course, from basic components to a fully integrated navigation pipeline.

  • Course Overview3:30

Requirements

  • Basic programming knowledge in Python or C++ (only simple syntax is needed).
  • Debug, test, and optimize robot navigation performance using best practices for sensor fusion, topic communication, and system architecture.

Description

Are you looking to build your own autonomous robot from scratch, but don’t know where to start?
This course is a complete, hands-on guide to robot navigation using affordable hardware, ROS, and real-world robotics algorithms. Whether you want to build delivery robots, indoor service robots, or autonomous vehicles for your research, this course will walk you step-by-step through every essential component of a working navigation system.

You will begin by learning the core hardware setup: Jetson Nano (or Raspberry Pi), Arduino controllers, wheel encoders, BNO055 IMU, and RPLIDAR. You will see exactly how each component connects, how power is managed, and how to verify that every sensor is publishing data correctly.

Next, you will learn how to build the complete navigation pipeline. We will cover ROS communication, TF frames, odometry generation, sensor fusion using an Extended Kalman Filter, and 2D mapping using Hector SLAM. You will also learn how to publish motion commands, tune a controller module, and integrate your robot’s hardware with high-level navigation logic.

A full section is dedicated to path planning, where we implement the RRT* algorithm and explain how sampling-based planners work in real navigation systems. You will see how to generate a collision-free path and then execute it using your robot’s motor control module.

By the end of this course, you will have a fully functioning robot navigation system capable of sensing, mapping, planning, and moving autonomously. All code, diagrams, and wiring references are included so you can reproduce the entire project on your own.

If you want practical robotics skills that let you build a real working robot—this is the course for you.

Who this course is for:

  • Beginners in robotics who want a practical, step-by-step introduction to robot navigation and control.

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