Introduction

This chapter introduces common map structures and how they support navigation planning. You will start by representing a continuous environment as an occupancy grid and defining how a robot is allowed to step between cells. From there you will learn how to estimate the remaining distance to a goal with heuristic functions such as Manhattan and Euclidean distance, then see how the A* algorithm combines that estimate with the exact cost already traveled to search efficiently toward a goal. The chapter closes by tying every piece together in a complete, runnable A* implementation. The goal is to build intuition for choosing planning methods that match task constraints.