In factory warehouses, intelligent forklift trucks are commonly used for cargo handling. Typically, pallets are required for transferring cargo. However, in the complex factory environment, the placement and shape of pallets are not fixed. Mechanical limitations often prevent the establishment of fixed positions for consistent pallet placement. Furthermore, common issues include pallet surface damage, the presence of covering films, and specially-shaped pallets.

So, how can we ensure that the smart forklift can accurately identify the position and shape of the pallet and fork it correctly? In response to these scenarios, SEER Robotics launched adaptive recognition and fork technology integrated with AI algorithms. Based on AI deep learning, the intelligent forklift gains advanced decision-making and execution capabilities, without the need for manual input of pallet-related information. This allows the forklift to think and act independently, like the human brain, improving work efficiency and easily adapting to various special handling scenarios.

After the intelligent forklift is equipped with a 3D depth camera, it can independently obtain the pallet and external information, analyze and process the data from the 3D depth camera based on AI deep learning, accurately identify the spatial pose of the hole contour of the pallet, and intelligently plan the fork path.

At the same time, interference is effectively filtered based on AI algorithms, reducing identification interference outside the pallet. In complex cases such as pallet damage, film coating, different shapes, and light changes, the intelligent forklift operation remains accurate and efficient, improving the efficiency and safety of cargo handling.

Functional Highlights:

1. Automatic Feature Learning: AI-based deep learning can automatically learn and extract key features from a large amount of data without manual design, enabling the identification system to better adapt to different job tasks and improve its generalization ability.

2. Powerful Data Fitting Ability: By using a 3D depth camera to collect image and point cloud data, complex models are built through multi-layer neural networks, with a powerful data fitting ability to capture complex patterns and relationships in the data, leading to more accurate identification and cross-fetching.

3. Wide Range of Application Scenarios: It can adapt to more special scenarios, such as film covering, appearance damage, different shapes, color changes, and light changes of the pallet. In these complex environments, it can still provide more reliable identification results and perform more accurate fork actions.

4. High Precision, High Efficiency, High Ease of Use: The effective use of GPUs and other specialized hardware accelerates the process, greatly improving the efficiency and accuracy of image recognition. No additional model files need to be configured, and it boots immediately to provide a convenient user experience.