Contents:
1. Small Obstacle Detection Failure
2. Front / Side Collision
3. Inaccurate Detection in Special Environments
4. Robot Contacts High-Risk Obstacles
5. Voice command recognition failure
Small Obstacle Detection Failure
1. Models without AI cameras:
Obstacles below 1cm (e.g. cables, wires) cannot be reliably detected. We recommend clearing such objects pre-cleaning.
2. Models with AI cameras:
Though it with AI camera recognition, please understand that the AI recognition requires learning with extensive model data for it to continuously optimize recognition accuracy and object types.
3. Magnetic strip cannot be detected. For avoiding, please set No-Go Zoness in the app.
Front / Side Collision
1. Confirm whether the surface of the robot's forward and side sensors is dirty. If so, please wipe it and try again.
👉 Freo X Ultra Example: Sensors Maintenance
2. Confirm the working condition of the robot's LiDAR.
🚫 Has the robot ever fallen? Or do you usually have the habit of lifting the LiDAR cover? This may cause the LiDAR to fail.
In this case, please move the robot to a flat place, manually press down the LiDAR protective cover, and see if it moves back. If not, contact support with a video.
3. The robot cannot work perfectly in a special environment with high reflection, strong direct light, or many blind spots, or an environment that is too dark. ↓
Inaccurate Detection in Special Environments
1. Highly reflective surfaces
The robot's radar may misidentify highly reflective surfaces like mirrors, polished metals and glossy tiles as actual space due to their high reflectivity, possibly causing reduced speed or collisions.
2. Light
When ambient light is dim, the robot automatically turns on its fill light. However, the fill light has limited range and intensity, which may still impair overall visual obstacle avoidance—potentially causing minor collisions from delayed recognition.
When the ambient light is strong, the module may fail to clearly perceive the surrounding environment, reducing obstacle avoidance capability.
3. Visual blind spots
When the robot encounters multiple low obstacles such as table legs or other obstacles, the front visual camera and right structured light may not fully cover all visual blind spots.
If obstacles are at the left edge, the visual blind spots may lead to collisions during obstacle avoidance.
✅ Please ensure that the machine mapping and working environment are normal.
Robot Contacts High-Risk Obstacles
When high-risk obstacles (data cables, power strips, power cords, etc.) form a narrow path with each other or with walls, and the robot tries to pass through this narrow path, it may rub against the high-risk obstacles in the process.
✅ Suggestions:
1. For robots with AI recognition function, open the real-view icon for high-precision recognition: Narwal Freo APP - [Settings] on the top - [AI obstacle avoidance] - Choose [Safty]
2. For all the models, you can try to set No-Go Zones.
Voice command recognition failure
Check if there are any obstructions over the three microphone sound inlets on the robot's radar cover. If any, remove any. If the error persists, contact support with a video can show the issue.