I understand that many robot vacuums use SLAM techniques to create internal maps and plan efficient routes. Could you explain, at a high level, what steps the mapping process involves, how maps are updated in real time, and what obstacle avoidance methods are most common? Also, what role does artificial intelligence play in optimizing cleaning? I'm interested in both the theory and implementation examples.
How do mapping and navigation algorithms work in robot vacuums?
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A year ago, I tried the Roborock S7, and shortly after setting it up, I realized that the mapping process isn’t some kind of drone magic—it’s a pretty logical sequence. First, the robot does a "blind" sweep using its spinning LIDAR; each pulse measures distance, and by combining angles, it generates a point cloud that the SLAM module converts into a 5 cm-per-cell raster map. That map is stored in the flash memory, and while it cleans, the unit keeps scanning with its infrared sensors and the same LIDAR to update it in real time: if it detects an area previously marked as "clear" but now has a new piece of furniture, the cell switches to "obstacle," and the path-planning algorithm (A* or D*) recalculates the route.
As for obstacle avoidance, the most common methods are "bump-and-turn," which uses mechanical bumpers to back up and turn, and vision-based detection (cameras with edge detection) that lets it dodge low objects without touching them. The AI comes into play when the robot learns from usage patterns: using lightweight neural networks, it recognizes high-dirt areas (based on the amount of particles detected) and adjusts the cleaning frequency, even predicting the best time to clean based on the home’s traffic history. In my case, after a few weeks, the S7 started spending more time in the kitchen area—where there were always food scraps—and avoiding the hallway when the cat crossed its path, all without me having to manually reprogram it.