스마트 내비게이션과 장애물 회피

벽과 가구의 지도만 그리는 데 그치지 않고, 카메라와 듀얼 레이저, 기계식 팔까지 최대한 활용해 전선과 신발, 반려동물의 실수를 파악하고 피해가는 로봇들입니다.

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    Samsung's Jet Bot AI+ combines a 3D sensor with Intel-developed AI to detect household items and avoid them during precision mapping.

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    Xiaomi's X20+ uses LDS laser navigation plus S-Cross structured-light to detect low obstacles even in dim rooms during cleaning.

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    TrueDetect 3D 3.0과 TrueMapping 2.0을 기반으로 만들어진 컴팩트한 옴니 도크 로봇으로, 약 6분 만에 100제곱미터를 매핑하고 3D 장애물 감지로 평면 센서 시스템이 놓치기 쉬운 낮은 잡동사니까지 식별한다.

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    eufy's X10 Pro Omni detects over 100 object types using AI.See obstacle avoidance, handling cables and toys while mapping and mopping.

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    HomeRun series uses cameras and D-ToF laser sensors for autonomous obstacle detection and navigation

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    Multiple models including 360 Vis Nav and Spot+Scrub AI use cameras and LiDAR for precise obstacle recognition

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    iRobot's Roomba Combo j9+ uses on-board camera and AI to recognize and steer around pet waste, cables, shoes, and household clutter during cleaning.

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    LG's CordZero combines LiDAR, RGB camera, and 3D sensor for precise room mapping and obstacle detection along its cleaning route.

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    V12 uses DuoDetect AI 3D obstacle detection with structured light 3D scanning at 5mm accuracy

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    S10 and S20 models use AI-powered cameras to identify 100 obstacle types including cables, socks, and pet waste

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    Dreame's L20 Ultra uses 3D structured-light and AI to recognize about 55 obstacle types, adjusting its cleaning path to avoid them.

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    5축으로 접히는 기계식 팔을 갖춘 세계 최초의 양산형 로봇청소기로, 팔을 펼쳐 작은 물건, 양말, 케이블, 가벼운 쓰레기를 집어 경로 밖으로 옮기며, 21,600개가 넘는 데이터 포인트를 활용하는 StarSight 2.0 주행 시스템이 이를 이끈다.

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    RCV 5 combines LiDAR, dual-laser system, and AI camera for precise flat obstacle avoidance

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    Lucy series vacuum uses dual 1080P cameras and depth sensors for AI obstacle avoidance, detecting objects as small as 1 inch

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    Z60 Ultra detects and avoids 200+ object types with dual-laser 3D mapping and RGB camera.

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    Shark's AI Ultra combines 360-degree LiDAR mapping with camera-based NeuroNav obstacle detection to browse around furniture and cords intelligently.

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    360도를 스캔하고 바닥 가까이를 감시하는 삼중 레이저 배열을 중심으로 만들어져, 단일 레이저라면 놓칠 만한 작고 낮은 장애물까지 포착하면서도 벽과 가구 다리에서 몇 밀리미터 이내까지 청소할 수 있다.

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    Tapo's RV30 Max Plus combines LiDAR mapping with IMU sensors to browse homes and avoid obstacles like furniture and cords.

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    프로시닉의 주행 성능에 초점을 맞춘 모델로, 8,000Pa 흡입력과 자동 먼지 비움 스테이션과 함께 장애물 회피를 위한 이중 레이저 시스템을 갖춰, 브랜드의 입문형 Q10보다 더 확실한 주행을 원하는 구매자를 겨냥한다.

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    범퍼 센서나 평면 레이저 스캔에만 의존하는 대신 3D 이미징으로 앞에 놓인 물체를 시각적으로 감지하며, 신발이나 반려동물 밥그릇 같은 일상적인 잡동사니에 로봇이 걸리지 않도록 특별히 설계되었다.

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    E20 Plus features DuoDetect AI 3D obstacle avoidance using dual-line laser for precise small-object detection

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    X-Plorer 120 AI uses laser and camera tech to detect objects as small as 2 centimeters.

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    Conga 7090 IA features dual cameras and AI to detect and recognize 200+ household objects for obstacle avoidance

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    Matic's robot vacuum uses only RGB cameras and an on-board Nvidia Jetson Orin chip, processing everything locally without relying on LiDAR.

Frequently asked questions

How do these robots handle small objects like cables or socks?

Many models use AI cameras and 3D sensors to recognize and steer around these items. Some advanced options even feature a mechanical arm that can pick up small objects like socks and cables to move them out of the cleaning path.

Can these vacuums navigate effectively in dark rooms?

Yes. Certain models use structured light technology to detect low obstacles even when lighting is dim. Others combine LiDAR with cameras to maintain precise mapping and navigation.

What kind of objects can the AI actually identify?

High-end models can recognize over 100 different object types, including pet waste, shoes, and toys. This allows them to adjust their cleaning paths to avoid specific types of household clutter.

How do the sensors work to prevent collisions?

These robots use a variety of technologies such as 3D scanning, LiDAR, and RGB cameras. Some use structured light with high accuracy to identify low-lying obstacles that standard mapping might miss.