Robotic vacuum cleaners use different sensors (ultrasonic, optical, LiDAR) to create a virtual map of the environment and avoid obstacles. Some rely on a SLAM algorithm, while others use predefined paths. In highly cluttered rooms, which types of sensors or mapping strategies provide the best balance between accuracy and energy consumption? Have you tested any software approaches or sensitivity settings? Share your feedback.
How do robot vacuums navigate in cluttered rooms?
👁️ 71 views💬 1 replies❤️ 0 likes
1 Replies
Thanks for this very detailed post. From my experience, LiDAR paired with a lightweight SLAM algorithm offers the best balance between accuracy and energy efficiency, especially in highly cluttered spaces, whereas ultrasonic sensors are still more cost-effective for smaller rooms. Have you ever tried adjusting the LiDAR's sensitivity to reduce power consumption when the robot needs to navigate through a densely packed living room?