Feature #12: Warehouse and Drop Points
Explore how to apply breadth-first search to precompute the shortest distances from all open spaces to the nearest drop points in a warehouse grid. This lesson helps you understand efficient pathfinding techniques for robot navigation in real-world logistics scenarios, optimizing route decisions by using BFS starting from multiple drop points at once.
We'll cover the following...
Description
We have mapped an Amazon warehouse into a rectangular grid. There are several shelves on the floor. Furthermore, there are drop points that connect the warehouse to the delivery vans. We have robots that are programmed to fetch items from the shelves and drop them off at the nearest drop point.
The warehouse is represented as a 2D array. A cell with -1 represents a shelf, a cell with 0 represents a drop point, and the infinite value represents an open space (corridor) that the robot can tread to move from shelves to the nearest drop points.
Robots need to navigate the warehouse to pick up items, one at a time, and drop them off at the nearest drop points. The distances from every open space to the nearest drop point need to be pre-computed for efficiency.
An open space in a warehouse is represented by an infinite value. In this case, we will consider an infinite value. The distance from any open space to the nearest drop point will not exceed this value.
Our task is to precompute the ...