Hands-On ROS for Robotics Programming

Robot Simulation and Programming Using ROS

Integrating a physical robot with a virtual simulation through the Robot Operating System (ROS) is one of the fundamental challenges in robotics engineering. This book provides a comprehensive guide to designing, simulating, and validating robotic systems in virtual environments before deploying them in real-world applications, enabling consistent and reliable robot behavior across both domains.

The book begins by introducing the GoPiGo3 mobile robot platform, including its sensors, actuators, and hardware architecture. It then demonstrates how to create a digital twin of the robot by developing a three-dimensional model from the ground up and simulating its operation within the Gazebo environment using the ROS framework. Building on these foundations, readers learn how to develop an autonomous mobile robot capable of perceiving, interpreting, and responding intelligently to its surroundings.

Beyond conventional robot programming, the book explores advanced learning techniques that enable robots to acquire new skills through interaction with their environment rather than relying solely on predefined code. It introduces key concepts in deep learning and reinforcement learning, illustrating how these artificial intelligence methods enhance perception, decision-making, and autonomous behavior.

By the end of the book, readers will have acquired the knowledge and practical skills required to design purpose-specific robotic applications and develop intelligent autonomous robots from the ground up.

Key Learning Outcomes

• Develop robots capable of perceiving and adapting to dynamic environments.
• Understand how robotic systems respond to real-world environmental conditions.
• Design complex robot behaviors by decomposing tasks into coordinated sequences of actions.
• Integrate sensor data with environmental context to generate adaptive and intelligent responses.
• Apply reinforcement learning techniques to enable robots to learn through trial-and-error interactions.
• Implement deep learning methods to improve environmental perception, object recognition, and autonomous decision-making.