Manufacturing industry Modular robots as drivers for Industry-X

Related Vendors

RobCo GmbH

FAULHABER Drive Systems

Taiwan Machine Tool & Accessory Builders' Association (TMBA)

Wuxi InfiMotion Technology Co., Ltd.

Anyone who still believes that employing several robotics experts and computer scientists is necessary for operating an industrial robot is mistaken. Modern platforms, AI, and other tools greatly simplify the use of these mechanical helpers.

For years, the Smart Factory has been a central topic in the manufacturing industry as demands continue to grow. In addition to striving for increased efficiency, challenges such as the ongoing lack of skilled workers, high customer expectations, strict quality standards, and growing regulatory requirements pose significant obstacles. To remain competitive, more and more companies are adopting technologies such as artificial intelligence (AI). Nevertheless, many production facilities struggle to keep pace with rapid digital developments, and in most factories, the implementation of Industry-X remains fragmented. 

Production hardware, especially robots, should not be overlooked. Because the integration of AI-supported robotics enables highly dynamic and flexible production concepts, reduces costs, and offers a solution to the shortage of skilled workers.

Modular robots, like those offered by the German company Robco, are extremely flexible and specifically tailored to the needs of the manufacturing industry, particularly for small to medium-sized enterprises. Their modular kit system allows these robots to be easily reconfigured and used for different applications whenever required. They can be remotely configured, implemented, and managed via a digital twin, with no need for extensive programming knowledge or specialized personnel thanks to no-code software. Modular robotic systems utilize open platforms with APIs and integrate IoT functionality to enable predictive maintenance. Software updates are conducted "over the air," ensuring straightforward updates and maintenance of the robots. 

Concrete application examples for modular robots

Quality Assurance through AI-Supported Modular Robots: Quality assurance of workpieces and components is a central challenge in manufacturing. In mass production, sampling inspections are common because manually checking each part would be uneconomical. The deployment of modular robots equipped with cameras or laser scanners and trained object recognition AI can monitor parts directly during the manufacturing process. For example, AI-supported modular robots are used for quality control of welds in Original Equipment Manufacturers (OEMs). A 3D laser profile sensor at the end of the robot arm enables precise object scans to detect flaws such as incorrect dimensions, pores, or interruptions. Automobile manufacturers employ this solution by integrating AI-based object recognition with the sensor data from welding robots to enable real-time analyses of each workpiece. This quality control also allows for root cause analyses to permanently eliminate sources of errors and supports the continuous improvement and automatic reconfiguration of welding robots to enhance product quality. 

Nvidia Omniverse—Synchronous Digital Twin: Digital twins in the Nvidia Omniverse precisely and realistically visualize the robot and its environment. This is facilitated by loading a 3D model of the robot in the Universal Robot Description Format (URDF) into the robot simulation program. A special extension enables a connection via Websockets to the robot, continuously reading the angular positions of each robot arm. This data transfer allows for real-time analysis and visualization in the Omniverse, enabling the digital twin to interactively engage with its physical counterpart. 

This technology offers numerous application possibilities:

• Remote monitoring of production: Production managers can monitor production processes, assess process efficiency, and make optimizations without needing to be physically present. This capability is particularly valuable for companies with multiple locations or in situations with limited access to the factory floor or large distances.

• Simulation of motion sequences: Pre-programmed simulations such as "weld seam inspection using a laser sensor" enable the planning and prediction of robot actions before the implementation of new production processes.

• Manual control via user interface: Users can manually control both the digital model and the physical robot via a web interface. This allows potential positioning of the robot in a virtual environment to be tested, optimized, and then implemented in reality.

The compact motion data of the robot arm allow for seamless transmission to a cloud infrastructure. Within this infrastructure, a powerful virtual machine is used to process the visualizations of the robot arm. Access is secured through a VPN connection, ensuring the integrity and security of the data. This setup is accessible via a web browser, enabling control from virtually any endpoint device. 

Assisted Operation of the Modular Robot with an XR Headset: The assisted operation of modular robots using an XR headset and the associated software offers an innovative way to familiarize users with controlling complex systems. By integrating Augmented Reality (AR), step-by-step instructions are provided directly in the user's field of view. Users receive virtual instructions in real-time, showing them how to perform specific actions. The XR headset guides the user through each step, from turning on the robot to readiness for operation, with the guidance dynamically adapting to the specific situation and environment. Virtual tiles, lines, and cues simplify the operation of the robot, even for individuals without prior experience. This flexibility allows for the customization of the training and operation process to suit different scenarios.

The method of assisted operation using an XR (extended reality) headset offers numerous advantages:

• Reduced training time: Users can be quickly and effectively trained without the need to study extensive manuals or undergo specialized training.

• Error reduction: New employees can familiarize themselves with the machinery in a virtual environment without actually operating it or disrupting ongoing operations. This reduces the risk of operational errors during the training phase and allows for safe training.

• Interactive learning experience: The interactive learning experience through visual and interactive guides enhances user understanding and retention by allowing them to better process and remember complex information.

• Simulation-based training for emergency situations: Using an XR headset, emergency scenarios can also be simulated, better preparing the operating staff for such situations and increasing safety.

Conclusion

The introduction of modular robots marks a significant step towards Industry-X in the manufacturing sector. Their flexibility and intelligence promise to elevate the automation of production processes to a new level. By integrating digital twins, it will be possible to virtually monitor and optimize production processes, leading to more efficient and agile manufacturing. Current use cases already demonstrate the immense potential of this technology. In the future, modular robots are expected to play a key role in leading the manufacturing industry into an era of flexibility, automation, and intelligence.

*Roman Hölzl is the co-founder and CEO of Robco

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