What is pathfinding?

Pathfinding is the process of finding a route or a series of steps that leads from a starting point to a destination, while avoiding obstacles. In computer terms, it’s an algorithm that tells a program how to move through a space-like a game map, a robot’s floor plan, or a network-so it can reach a goal efficiently.

Let's break it down

Think of a maze. You stand at the entrance (the start) and want to get to the exit (the goal). Pathfinding algorithms look at each possible move (up, down, left, right, diagonal, etc.), keep track of where they’ve been, and decide which direction brings them closer to the exit. Common steps include:

  • Representing the space as a grid or graph of nodes.
  • Assigning a cost to moving from one node to another.
  • Exploring nodes in an order that tries to minimize total cost.
  • Stopping when the goal node is reached and then retracing the steps taken.

Why does it matter?

Without pathfinding, games would have characters that walk into walls, robots would bump into furniture, and navigation apps would give useless directions. Good pathfinding makes virtual worlds feel realistic, helps autonomous machines move safely, and saves time and resources in logistics and transportation.

Where is it used?

  • Video games (enemy AI, NPC movement, puzzle solving)
  • Robotics (vacuum cleaners, warehouse robots, drones)
  • Mapping and navigation apps (Google Maps, GPS routing)
  • Network routing (finding optimal data paths)
  • Logistics and supply chain (optimizing delivery routes)

Good things about it

  • Enables realistic and intelligent movement in digital environments.
  • Improves efficiency, saving time, fuel, or computational resources.
  • Can be adapted to many different spaces: 2D grids, 3D worlds, or abstract graphs.
  • Many well‑studied algorithms (A*, Dijkstra, BFS) make implementation easier.

Not-so-good things

  • Complex environments can make calculations slow, especially on limited hardware.
  • Some algorithms may get stuck in local minima or produce sub‑optimal routes.
  • Requires a good representation of the space; inaccurate maps lead to bad paths.
  • Real‑time pathfinding for many agents simultaneously can be challenging to scale.