When Vector Fields Steer You Wrong: Core Ideas of Vector Field Navigation
You drop a robot in a room full of chairs, boxes, and a moving person. You want it to reach the far door without bumping into anything. The classic solution: plan a path, then follow it. But what if the person shifts? The path is suddenly blocked, and the robot freezes. Vector floor navigation (VFN) offers a different philosophy: don't plan—flow. The robot moves as if pushed by an invisible fluid, always drifting toward the goal while being repelled by obstacles. No replanning, no waypoints. Just continuous reaction. This article unpacks the core ideas of VFN: why it matters today, how it works under the hood, a concrete walkthrough, edge cases that bite you, and its real-world limits. If you are evaluating navigation algorithms for a drone, a robot arm, or an autonomous vehicle, this will give you the practical intuition—and the pitfalls—before you commit to code.