The heart of autonomous navigation is path planning—the ability for a robot to autonomously determine the best path to its destination while avoiding obstacles. Path Planning with ROS2 provides an in-depth guide to implementing and optimizing A* (A-star) and RRT (Rapidly-exploring Random Trees) algorithms for autonomous robots in dynamic environments using ROS2 and Gazebo.

This book equips you with the practical skills to build and optimize motion planning algorithms, navigate complex environments, and deploy real-world applications. With detailed examples and a focus on ROS2 integration, you’ll learn how to create smooth, efficient, and reliable paths for robots, from mobile platforms to manipulators.

Inside, you’ll master how to:

Implement the A* algorithm for optimal pathfinding in static environments
Use RRT for planning in high-dimensional spaces with complex obstacle configurations
Integrate path planning with ROS2’s navigation stack for real-time execution
Optimize path planning algorithms for efficiency and real-time performance
Simulate and test path planning solutions in Gazebo, adjusting for real-world dynamics
Combine global and local planning with obstacles, dynamic changes, and sensor feedback
Customize planners for specific use cases: mobile robots, drones, and more
Visualize planned paths and robot motion in RViz and Gazebo simulations

Whether you’re building autonomous cars, drones, or warehouse robots, this book will teach you to plan and execute autonomous motion that is both safe and efficient.

🧠 Ideal for robotics engineers, AI developers, and autonomous navigation specialists
📦 Includes code examples, optimization tips, and simulation setups in ROS2 and Gazebo
⚙️ Supports ROS2 distributions: Foxy, Humble, Iron + Gazebo, and RViz integration

Master path planning. Build smarter autonomous robots. Move with precision.

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