Robot Autonomous Navigation

Autonomous navigation is where a mobile robot starts to feel truly intelligent: it receives a goal, decides on a route, and moves safely toward it. This course explains the logic behind that behavior.

You won't just learn how to launch a navigation stack. You will understand the decision-making and planning ideas underneath it, then see how they come together inside Nav2, the standard navigation framework in ROS2.

Whether you aim to work on mobile robots professionally or want to understand what happens under the hood of autonomous systems, this course connects the theory to real navigation behavior.

• Why Finite State Machines become hard to scale, and how Behavior Trees solve it with ticks, Sequence and Fallback nodes, XML, and the Blackboard

• Path planning versus motion planning, robot footprint, obstacle inflation, and the Configuration Space

• Turning a map into a graph: grid discretization, resolution trade-offs, and edge weights

• Graph search algorithms DFS, BFS, and A*, traced step by step with stacks, queues, and cost functions

• Costmaps and their static, obstacle, and inflation layers, and how they differ from occupancy grids

• The Nav2 architecture: BT Navigator, action servers, planner and controller servers, and the Lifecycle Manager

• Creating a navigation package with planner, global Costmap, and BT Navigator configuration, then launching path planning

• Motion planning algorithms such as DWA and Regulated Pure Pursuit, and configuring the DWB controller and local Costmap

• Analyzing the default Navigate To Pose Behavior Tree, including replanning, planner and controller selectors, and recovery actions such as Clear, Spin, Wait, and BackUp

• Testing path following and recovery behaviors in simulation