Path Planning and Mapping: Flood Fill to SLAM

Reactive navigation gets a robot around a room without ever knowing where it is. That runs out quickly: a purely reactive robot cannot be told go to the kitchen, and it will happily loop forever in a room shaped like a horseshoe. Planning is the answer, and it needs something reactive control never has—a map.

This path starts where the map is free. In a maze, flood fill floods distances outward from the goal and the robot simply walks downhill, replanning the instant it discovers a wall it had assumed was open. Then the world gets messier: A* plans across an occupancy grid where cells have costs, obstacles need inflating, and the heuristic decides whether your planner is fast or merely correct.

The last node is the honest one. Every planner above assumes you have a map, and a real robot has to build one while moving on wheels that lie to it. SLAM is what happens when mapping and localization refuse to be separated, and loop closure is what stops the map bending as odometry drifts. An IMU earns its place here for the same reason: it measures rotation directly, which wheels that slip simply cannot.