Robot Operating System (ROS)
ROS offers a wide range of functionality, including hardware abstraction, device drivers, communication between processes, package management, and more.
One of the key strengths of ROS is its modularity and community-driven development. It simplifies the process of building and controlling robots by providing a standardized framework for integrating various components like sensors, control algorithms, and user interfaces.s.
Lesson 01
Introduction to ROS
ROS2, or Robot Operating System 2, is an open-source middleware framework designed for building and developing systems and applications for robots. It is the successor of the original Robot Operating System (ROS). ROS 2 was created to address some of the limitations of ROS 1 and provide a simpler yet responsive platform for robot development.
Lesson 02
TurtleSim
TurtleSim is a simple simulation environment in ROS that uses a character called a "turtle" that can be controlled using commands.
TurtleSim is used as a training tool for beginners who are just starting to learn ROS.
Lesson 03
ROS2 Work Space
TurtleSim is a simple simulation environment in ROS that uses a character called a "turtle" that can be controlled using commands.
TurtleSim is used as a training tool for beginners who are just starting to learn ROS.
Lesson 04
Gazebo and RViz
​Gazebo is a simulation environment or simulator used in the Robot Operating System for the analysis, training, and testing of robots and autonomous systems. RViz or ROS Visualization is used to view and visualize data from sensors, robots, and various parts of the robotics system. RViz is also used for debugging and monitoring robotics applications. RViz includes interactive tools that facilitate tasks such as path planning and setting waypoints for robot navigation.
Lesson 05
Slam Toolbox
Simultaneous Localization and Mapping (SLAM) is a process in which a robot determines its own location (localization) while simultaneously mapping its environment. SLAM is a fundamental concept in robotics navigation. SLAM begins with the creation of a map of the environment where the robot will move. Our robot will use a LiDAR sensor to scan the surroundings and record data on the map. The map will be used for the robot's navigation within the environment.
Lesson 06
SLAM Navigation in Gazebo
Adaptive Monte Carlo Localization (AMCL) is an algorithm that processes sensor data to determine the robot's location using probabilistic techniques that indicate the level of certainty of its location estimate.
Lesson 07
Robot Architecture
Here is the actual configuration of our ROS2 compatible robot. You can see the A2 RP Lidar mounted on the top part, the LCD display, and its connection wires. Below it is the Raspberry Pi 4B. We also added an auxiliary fan for our Raspberry Pi since it can heat up while processing data.
Lesson 08
Wheel Encoders
A wheel encoder is a sensor device used in robotics and automation to measure the rotational movement of a wheel or shaft. It provides information about the wheel's position, speed, and direction of rotation. Wheel encoders are a fundamental part of motion control systems, enabling precise and reliable monitoring of wheel or shaft movement, which is essential in applications requiring accurate position and speed control.
Lesson 09
PID Control
PID control is a feedback control system widely used in various industrial, manufacturing, and automation processes. PID stands for Proportional-Integral-Derivative, which are three fundamental components or parameters that make up this control system. It's designed to regulate a system's output to a desired setpoint by continuously adjusting the input, taking into account the current error.
Lesson 10
Configuring ROS2 Compatible Robot
At this point, we need to calibrate our robot's sensors and configure their measurements to match the data expected in the RViz simulation environment. We'll start by creating a workspace and downloading ROS2 packages, calibrating motor encoders, lidar, and robot dimensions.
Lesson 11
SLAM Navigation
Simultaneous Localization and Mapping (SLAM) navigation is a critical technology used in robotics to enable autonomous navigation and mapping of an environment. It's a fundamental technology for robots operating in real-world scenarios, contributing to their ability to move safely and effectively.