MASON

In global freight logistics, standardized sea containers are the primary mode of transport for general cargo, including food products. These containers are typically loaded manually, directly stacking individual packages without the use of pallets. While this method avoids the need for additional load-securing materials like straps or wrapping film thus cutting costs and reducing PVC waste, it relies entirely on human labor to tightly pack the goods. The challenge arises from the complexity and physical demands of this manual process. Workers are required to fit packages precisely against container walls using methods like pressing, wedging, and compressing—techniques that are inherently difficult to replicate with current robotic systems. This labor-intensive method stands in stark contrast to the high levels of automation already present in modern distribution centers.

Despite the rapid advancement of Industry 4.0 technologies, there is currently no robotic solution capable of performing this task with the required speed, precision, and reliability. Several key challenges prevent automation such as Confined working space inside containers Irregular package shapes and unpredictable loading configurations.

Furthermore, employees in this sector face difficult working conditions, especially in refrigerated environments typical for food logistics. This results in high physical strain, increased staff turnover, and elevated absenteeism—all of which increase operational costs and disrupt productivity. The lack of a robotic system capable of handling these tasks has been a critical bottleneck for logistics companies, particularly SMEs in high-wage regions like Germany, which must remain competitive on the global stage without increasing labor costs.

Project Work:

To address these challenges, this project developed a fully automated approach to the container loading process using state-of-the-art robotics and computer vision technologies. The core innovation lies in a new system architecture that allows robots to:

• Recognize the shape, size, and orientation of each package using AI-driven visual analysis
• Grasp and lift packages accurately within confined environments
• Precisely position packages to maximize space utilization and ensure stable stacking
• Adapt dynamically to changing package configurations and container conditions in real time

By integrating advanced algorithms and robotic control methods, the system can handle complex loading scenarios with a high degree of reliability and speed. The result is a process that significantly reduces manual labor, minimizes risk of injury, and increases throughput, loading containers more quickly and efficiently than traditional manual methods.

This project targets enhancing the automation of food container loading, a task long considered too complex for robotic systems due to its unstructured nature. Through the innovative combination of AI, robotics, and adaptive control, the developed solution provides a pathway to transforming logistics operations making them faster, more efficient, and more sustainable.

Für weitere Informationen und zur Verfügbarkeit des Schlussberichtes wenden Sie sich bitte an die Forschungsvereinigung Forschungsgemeinschaft Intralogistik/Fördertechnik und Logistiksysteme (IFL) e.V. (IFL e.V.). Für Rückfragen steht Ihnen Nils Ziebach unter nils.ziebach@vdma.org zur Verfügung.

Funding

This project is part of the MASON project "Design and evaluation of a mobile handling robot for location-independent order picking in the food industry". The MASON project was carried out in the framework of the industrial collective research program (IGF no. 01IF22403N). It was supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK) based on a decision taken by the German Bundestag.

The University of Siegen contributed to the project by developing a system for object detection and classification in warehouse environments using depth camera technology. This approach enables more accurate recognition of objects with varying shapes and sizes, even in cluttered or dynamic settings, by leveraging 3D spatial data. Additionally, the university investigated wireless communication technologies such as Wi-Fi and 5G within heterogeneous networks and proposed a communication architecture that supports Time-Sensitive Networking (TSN) to ensure reliable, real-time data exchange across the warehouse system.