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Intelligent robotics for industrial laundries

Challenge

High Variety of Container Types

In real production environments, containers often come from different manufacturers. They vary in type, size, and geometry. Some may be slightly deformed or positioned at an angle. The system must therefore analyze every container individually. Rigid programming is not enough. Instead, the system must recognize container type, shape, and position dynamically in all six degrees of freedom.

Precision Across Large Measurement Volumes

The containers and components involved can be quite large. At the same time, placement points must be identified with millimeter accuracy. Only precise positioning allows the robot to place parts safely and reliably. The system therefore needs to capture large workspaces while maintaining high measurement accuracy. High-quality 3D data across the entire working area is essential.

Collision-Free Robot Guidance

Precise container detection alone is not sufficient. The system must also detect potential obstacles. These can include protruding edges, foreign objects, or damaged containers. Such irregularities may block the loading process or cause collisions. The system must therefore identify these obstacles in advance. It then adapts the robot’s motion accordingly or stops the process if necessary.

Integration Into Existing Production Processes

Automation must integrate seamlessly into existing production lines. Image capture and data analysis therefore have to run in real time. Additional cycle time is not acceptable in high-throughput manufacturing environments. At the same time, the system should provide valuable data for quality management. For example, it can document container conditions and support full process traceability.

High Requirements for Sensors and Robustness

Industrial environments place high demands on hardware. Camera systems must capture large objects reliably and deliver measurement data quickly. They must also withstand dust, humidity, and mechanical stress. Only robust and reliable sensors can ensure stable operation and long-term process safety.

Application

Industrial laundries already rely heavily on automation to process enormous volumes of textiles. However, certain steps still require manual work. Machines can fold laundry efficiently. But feeding textiles smoothly and without wrinkles into folding machines is still usually done by employees. This work is repetitive and physically demanding. At the same time, it drives labor costs. Labor shortages, fluctuating workloads, and seasonal demand peaks make the situation even more difficult for operators. The Munich-based startup sewts addresses this challenge with its VELUM system. The solution automates the feeding of textiles into folding machines. At the heart of the system is powerful 3D machine vision. It allows robots to detect randomly arranged textiles, analyze them, and pick them precisely. Additional 2D cameras provide further visual information.

Precise 3D Capture of Deformable Materials

Automating textile handling is difficult because fabrics constantly deform. They do not have a stable geometry. Their shape changes continuously. Conventional robotics systems often struggle with this behavior. The integrated 3D cameras solve this problem. They generate precise depth data that describes the shape, position, and surface structure of the textiles. The system can therefore analyze laundry even when it lies randomly in containers or on conveyor belts.

Based on this data, the software detects important features such as edges, corners, or seams. It then calculates suitable grasping points. The robot can pick up the textile, unfold it, and feed it smoothly into existing folding machines. The 3D data also ensures stable performance across different materials, textures, and positions.

Multi-Camera System for Reliable Automation

Depending on the system configuration, several 3D cameras monitor the workspace. This expands the field of view and increases accuracy. Sensor quality and industrial robustness are critical. The cameras must deliver reliable data even when textiles vary in position, size, and shape. By combining precise 3D sensing with intelligent software, the system understands how fabrics behave during handling. It can react immediately and adjust its movements in real time.

This makes it possible to automate even demanding tasks such as unfolding terry towels reliably and consistently. Overall, the solution closes a major automation gap in industrial laundries. It reduces manual work, increases productivity, and significantly improves operational efficiency.

Outlook

Systems such as VELUM enable industrial laundries to increase throughput significantly. At the same time, they reduce dependency on available labor. Operators can increase the productivity of existing facilities without hiring additional staff.

In the future, similar solutions could also handle garments such as shirts or trousers. Achieving this requires a deeper understanding of textile material properties. Advanced material simulations already support this effort. They allow engineers to model how flexible fabrics behave during robotic manipulation.

In the long term, these technologies could reshape the entire textile industry. Highly automated production may allow manufacturers to move operations closer to consumer markets. This could reduce transport distances, stabilize supply chains, lower CO₂ emissions, and decrease overproduction.

The underlying technology is also suitable for many other flexible materials. This opens up new applications in various industries. Continuous advances in machine vision and artificial intelligence will further accelerate this development and unlock new automation opportunities.

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