The pallet robot can accurately identify the target stacking position and cargo height. Here are specific instructions:

1. Target stacking position recognition
The depth camera or laser scanner equipped on the 3D vision positioning robot can generate a 3D point cloud map of the work area, and accurately calibrate the preset creole position of the pallet shelf through feature matching algorithms such as contour recognition and corner detection.
The spatial coordinate transformation converts the coordinates of the center point of the creole recognized by the visual sensor into real-time coordinates in the robot base coordinate system through a calibration matrix, guiding the movement of the robotic arm to millimeter level accuracy.
If there is a slight deviation in the placement of the tray through the dynamic correction mechanism, the system will recalculate the actual boundary of the creole through edge detection algorithm and automatically correct the grasping path.

2. Height recognition of goods
Vertical distance measurement utilizes ToF (Time of Flight) sensors or structured light projection to directly measure the vertical distance between the top of the cargo and the sensor reference plane. Combined with the height data of the pallet bottom, the total height of the cargo stack is calculated in real time.
Surface contour reconstruction obtains the three-dimensional contour of the cargo surface through multi angle scanning, identifies the highest point position during irregular stacking, and avoids height misjudgment caused by cargo tilting.
After the height dynamic calibration of each layer of goods stacking, the system immediately updates the height reference value to ensure that the cumulative error of subsequent height detection approaches zero.

3. Anti interference and adaptability
Active light sources with ambient light suppression (such as infrared lasers) combined with narrowband filters can eliminate the interference of natural light changes on measurements.
Material adaptability For high reflective (metal packaging) or light absorbing (black foam) goods, the system automatically adjusts the light source intensity and exposure parameters to ensure the integrity of point cloud data.
In the case of partial occlusion, historical data interpolation and adjacent feature point estimation are used to maintain the continuity of height and position data.

4. System level accuracy assurance
Multi sensor fusion models combine vision and close range laser rangefinders to cross validate key position data and eliminate single sensor errors.
The high repetitive positioning accuracy (± 0.5mm level) of the motion mechanism of the mechanical repetitive positioning compensation robot provides physical execution guarantee for visual recognition results.
After the real-time closed-loop feedback capture or placement action is completed, a second scan verification is immediately performed. If the deviation exceeds the threshold, the correction process is triggered.

5. Typical application verification
The high-level shelves are stored in a three-dimensional warehouse of more than 10 meters, accurately identifying the height of each layer of shelves and the depth of tray embedding to ensure the safe insertion of fork teeth.
When mixed stacking boxes of different sizes, real-time tracking of the boundary and center of gravity position of each layer is carried out to prevent the collapse of the stacking type.
Automatic loading and unloading vehicles adapt to uneven ground conditions of truck compartments, dynamically adjust lifting height and advance and retreat distance, and achieve collision free loading and unloading.