Vacuum Robot Decision Making: How Machines Navigate Space
The Problem of Autonomous Navigation
A vacuum robot operating in a home faces a surprisingly hard problem: navigate an unknown, dynamic environment, avoid obstacles, cover every reachable surface, and return to the charging dock — all without a map.
SLAM: Simultaneous Localization and Mapping
Modern high-end robots solve this using SLAM — Simultaneous Localization and Mapping. The robot builds a map of its environment while simultaneously figuring out where it is within that map. LiDAR sensors sweep 360 degrees and compute distances to obstacles, generating a point cloud that is stitched into a 2D floor plan.
Sensor Fusion
Lower-cost robots rely on sensor fusion instead of LiDAR. Accelerometers track movement. Cliff sensors detect stairs. Bump sensors detect collisions. An algorithm weighs all of these inputs to make navigation decisions.
Coverage Algorithms
Random bounce navigation — the classic pattern — is inefficient. Modern robots use systematic coverage algorithms: boustrophedon (row-by-row) paths, spiral patterns for open areas, and wall-following for edges. The goal is full coverage with minimal redundancy.
Decision Trees in Practice
At the core of robot behavior is a decision tree. If a cliff is detected, back up and turn. If stuck, try a liberation sequence. If battery is low, navigate to dock. These rules compose into surprisingly capable behavior.
Conclusion
What appears to be simple housework involves a rich stack of sensing, mapping, planning, and execution. The humble vacuum robot is a useful lens for understanding how machines reason about the physical world.