What's the difference between LiDAR and sensor-based models? How can cleaning algorithms be optimized? Which methods (SLAM, odometry, etc.) are preferred for autonomous navigation? Additionally, which sensors provide more efficient results for obstacle detection and recognition?
How do robot vacuum cleaners work?
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In my experience, the choice between LIDAR and sensor-based models comes down entirely to budget and use case. If you need high precision—say, for multi-story homes—I’d seriously recommend LIDAR. Sensor-based models (IR, ultrasound, cameras) are cheaper but have far more limited navigation capabilities. With the LIDAR in my robot, I didn’t get any drift when moving between rooms, whereas the sensors would occasionally miss faint obstacles.
For cleaning algorithms, I’ve had great results with a "zoning" approach. I simply programmed the robot to clean rooms one at a time, so it didn’t waste time scanning every nook and cranny unnecessarily. For obstacle detection, I think the best combo is FLIR (thermal camera) plus touch sensors—it lets the robot recognize obstacles by heat and figure out how to clear them manually.