As Industry 4.0 and intelligent manufacturing advance rapidly, automated warehousing has become key for companies to boost efficiency and cut costs. Autonomous forklifts, as a crucial part of automated warehousing, reduce reliance on labor and significantly enhance operational safety.
Pallet recognition technology is essential for the efficient operation of autonomous forklifts in automated warehousing. Pallets, favored for their low cost and widespread use, serve as the most common handling tool. With advanced sensor and image processing technologies, pallet recognition systems enable autonomous forklifts to locate pallets, ensuring efficient and precise operations while minimizing human errors accurately.
Autonomous forklifts face challenges during pallet handling, such as complex warehouse environments (obstacles, uneven lighting, etc.) and diverse pallet types. Traditional barcode and QR code recognition methods can be limited in some environments, while 3D ToF (Time-of-Flight) camera technology offers higher adaptability, especially when dealing with non-standard pallets or positional deviations.
MRDVS PalletPro Pallet Recognition System, powered by 3D ToF vision technology, provides an effective solution for complex warehousing environments. With intelligent algorithms, the system precisely identifies the location and orientation of pallets, demonstrating excellent adaptability to various conditions and enabling autonomous forklifts to transport pallets efficiently.
The PalletPro system integrates recognition algorithms directly into the camera, allowing for real-time processing of depth data. Compared to traditional methods, this integrated design significantly improves computational efficiency and response speed, enabling autonomous forklifts to quickly and effectively complete pallet recognition and transport tasks, greatly enhancing operational productivity.
The 3D cameras are installed between the two arms of an autonomous forklift and use automatic calibration technology to accurately determine the position and orientation of pallets relative to the forklift. Based on point cloud features in the depth data, the PalletPro system can identify the legs and crossbars of pallets, supporting standard two-legged pallets, four-legged pallets, and scenarios involving stacked pallets. It also supports customized solutions for legless or irregularly shaped pallets, enhancing compatibility and flexibility in diverse applications.
Designed for easy and quick deployment, the PalletPro system requires no algorithm development experience. With simple tutorials, users can quickly get started. The system is also compatible with Euro-standard pallets and is suitable for high-bay storage and complex stacking scenarios, delivering real-time and accurate pallet recognition results to meet various warehousing needs.
MRDVS’s pallet recognition solution performs exceptionally well under complex lighting conditions, especially in outdoor environments with strong lighting. The system utilizes the ToF depth camera M series equipped with a 940nm infrared emitter. This near-infrared light at 940nm offers strong interference resistance, preventing visible light from affecting depth data and ensuring the stability and accuracy of recognition results.
Video of outdoor testing
As a global leader in fiber optic communications, FiberHome Communication Technology uses autonomous forklifts equipped with MRDVS’s PalletPro system in its smart factory. The system enables automated pallet recognition and handling, providing real-time pallet location information and adjusting vehicle posture, improving FiberHome Communication Technology’s logistics automation.
China Jushi, a global leader in the fiberglass industry, faced challenges with non-standard metal pallet recognition as it advanced its smart manufacturing. MRDVS PalletPro system, integrated with Lanxin Technology’s autonomous forklift solution, successfully solved this issue, significantly enhancing the automation and intelligence of their warehousing logistics.
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