Which cleaning path planning and sensor selection approach for robot vacuums do you prefer? Which algorithm is more reliable for different floor plans and furniture layouts?

26 Tem 09:45

Most robot vacuum cleaners on the market today use two main path algorithms: random collision-based "jump cleaning" and SLAM (Simultaneous Localization and Mapping) systems. The former is simple to implement and low-cost but often misses corners and edges. The latter relies on laser or vision sensors to create room maps, enabling more efficient coverage but is more sensitive to lighting changes and furniture rearrangements. Sensor choice also affects obstacle avoidance and floor-level detection. Which approach do you prefer in practice? In multi-story homes or cluttered spaces, how do you balance cost with cleaning performance? Share your experiences and thoughts! 😊