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Automatic loading and unloading by 3D container inspection

Used products:C57

Four "eyes" see better than two

Robots perform monotonous processes as well as unpleasant, constantly repetitive tasks with exceptional reliability. Combined with machine vision, they evolve into “seeing” assistants that effectively support human workforce. In quality assurance, they inspect components, assist with assembly and positioning processes, identify errors or deviations in production, and thereby increase the performance of entire manufacturing lines.

An automotive manufacturer is leveraging these capabilities to optimize the cycle times of its press shops. Together with this manufacturer, VMT Vision Machine Technic Bildverarbeitungssysteme GmbH from Mannheim, Germany  developed the robot-based 3D measurement system. Named FrameSense, it enables fully automated loading and unloading of containers. The system allows press parts to be placed into containers or removed from them precisely and safely. Four of our Ensenso 3D cameras provide the required image data and thus form the basis for automated process control.

Application

The workflow that FrameSense is designed to automate is found in many production processes.
A component leaves a machine, here a press, and is transported via a conveyor belt to a container where it is stacked. Once the container is completely filled, it is transferred to the next production step, such as assembly in a vehicle.

Until now, placing the parts into the container has been carried out by workers. Although the task appears simple at first glance, it is actually considerably more demanding. Before placing the component, a suitable free position must first be identified. In addition, potential obstacles, e.g. locking mechanismsm, must be taken into account or removed. In addition, the condition of the container needs to be checked for damage.

In the future, a robot with integrated machine vision will perform all these steps, an ambitious technical job. What makes things even more challenging: the containers used come from different manufacturers. Thus, they feature various designs, and can differ significantly in size and shape.

Challenge

Fully automated container loading and unloading in a running production environment is highly demanding. Sensors, data processing, and robotics must work together seamlessly.The system must detect different container types, assess their condition, and place components precisely. At the same time, it must avoid collisions and keep the production flow running without delays

Broad variety of Containers

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 itself 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 an option 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 cope with dust, humidity, and mechanical stress. Only robust and reliable sensors can ensure stable operation and long-term process safety.

Outlook

By combining automated container handling with integrated 3D inspection, FrameSense replaces previously manual workstations with reliable automation. This becomes increasingly important as skilled labor shortages continue to grow. The system helps manufacturers increase efficiency while securing long-term production stability especially in the automotive industry and similar sectors.

At the core of the solution is powerful 3D camera technology. It delivers the precise spatial data required for demanding automation tasks.This is particularly valuable in intralogistics, where robots must pick components from larger distances and across large measurement volumes. High projector power and high sensor resolution offer clear advantages here. For other applications, such as depalletizing or bin-picking in logistics, systems with lower resolution but higher speed may become attractive alternatives.

Overall, the combination of high-performance sensors and intelligent robotics opens new opportunities for advanced automation. It accelerates the adoption of “seeing” robots in production and logistics. At the same time, it reduces manual workloads and enables more flexible, robust manufacturing processes.

Miniar Brenner
Director Business Development & Sales Ensenso
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