Researchers at Osnabrück University of Applied Sciences in Germany have launched a two-year initiative to develop an AI-powered robotic system capable of identifying and sorting smart textiles for recycling. The project addresses a growing sustainability challenge as garments embedded with electronics become more common in consumer wearables, industrial uniforms, and automotive applications.

The initiative, known as ReSiST-AR, is backed by regional development funding and aims to automate the detection and separation of e-textiles from conventional clothing streams. As smart fabrics integrate sensors, wiring, and electronic modules, traditional textile recycling systems struggle to process them safely and efficiently.

Without automated sorting, many of these garments risk ending up in landfills or being shipped abroad for low-cost manual processing.

Teaching Robots to Recognize Soft, Complex Materials

Unlike rigid materials such as metals or plastics, textiles present unique challenges for robotics systems. Fabrics are flexible, irregularly shaped, and often tangled or layered when placed on conveyor belts. Smart textiles add further complexity by embedding electronic components that may be hidden within seams or woven into fibers.

The research team is developing a robotic platform equipped with multispectral cameras and 3D sensors capable of scanning garments in mixed piles. AI-based material classification algorithms analyze the captured data to distinguish between fabric types and detect embedded electronics.

The goal is to enable robots to identify smart garments regardless of how they are positioned or folded. This requires machine learning models capable of interpreting varied visual and structural cues in real time.

Automation engineering researcher Steffen Greiser, who leads the project, noted that manual textile sorting is labor-intensive and often outsourced internationally, raising both environmental and ethical concerns. By automating the process, the team hopes to create regional recycling loops that reduce transportation and improve sustainability.

Designing Smart Textiles for Future Recyclability

Beyond sorting, the project also examines how smart textiles can be designed to simplify recycling. A separate research team is analyzing different integration methods, including sewn-in electronics, embroidery, and welded components, to determine how sensors and circuits can remain durable during use while being easier to remove at end of life.

This design-for-recycling approach reflects a broader shift in sustainable manufacturing, where product architecture is increasingly shaped by lifecycle considerations.

Guidelines developed through the project could help manufacturers create smart textiles that balance performance, user requirements, and recyclability. By embedding sustainability principles into product design, the initiative aims to prevent future waste streams from becoming unmanageable.

Robotics Expands into Circular Economy Applications

The ReSiST-AR project highlights how robotics and AI are moving into environmental and circular economy applications. Automated waste sorting has traditionally focused on rigid materials such as plastics and metals. Smart textiles introduce new technical demands that require advanced sensing and AI interpretation.

By combining robotics with multispectral imaging and AI-driven classification, researchers are building systems capable of operating in complex, variable environments where traditional automation struggles.

The project also involves collaboration with regional robotics and textile companies, allowing researchers to test prototypes in real industrial settings. These partnerships aim to accelerate commercialization and ensure that the technology can integrate into existing recycling infrastructure.

As wearable electronics and connected garments continue to proliferate, scalable recycling solutions will become increasingly necessary. The Osnabrück initiative offers an early example of how physical AI systems can support sustainability goals by automating complex sorting tasks that were previously dependent on manual labor.

The Hefei Metro system in eastern China has introduced a network of robot dogs, drones, and AI-powered service robots as part of a broader initiative to modernize urban rail operations. The deployment represents one of the most visible examples of robotics being integrated into public transit infrastructure to improve safety, inspection efficiency, and passenger services.

The autonomous systems now operate across stations and tunnels in Hefei’s metro network, assisting human staff with tasks ranging from security patrols to infrastructure inspections. City authorities say the program aims to improve operational reliability and reduce the risk of accidents in one of the region’s busiest transport systems.

The initiative reflects a broader shift toward robotics-driven infrastructure management as cities adopt smart technologies to handle growing urban populations.

Robot Dogs and Drones Automate Inspection Tasks

Among the most prominent additions to the metro system are quadruped robot dogs equipped with cameras and environmental sensors. These machines patrol station platforms and corridors, scanning for obstacles, safety hazards, and abnormal conditions.

Unlike traditional human patrols, the robots can operate continuously and monitor multiple areas simultaneously. Their sensors allow them to identify potential issues such as objects left on platforms or equipment anomalies that could affect operations.

Drones are also being used to inspect tunnels and track infrastructure. Flying inspection systems can access areas that are difficult or dangerous for workers to reach, capturing visual and sensor data used to assess equipment conditions and detect potential maintenance problems.

Together, the robotic systems form what metro officials describe as a “robot cluster” supporting operational safety and infrastructure monitoring.

Robotics Expands into Public Transportation Infrastructure

In addition to inspection robots, Hefei Metro has introduced humanoid service robots that interact with passengers. These robots provide directions, answer questions, and assist commuters navigating stations.

The deployment illustrates how robotics can support both operational tasks and customer service in public infrastructure. While robots handle routine inspections and monitoring, human staff remain responsible for supervision, emergency response, and complex operational decisions.

Such systems are particularly valuable during peak travel periods, including major holiday travel surges, when passenger volumes increase dramatically.

By automating routine monitoring tasks, metro operators can allocate personnel more efficiently while maintaining high safety standards.

Smart Transit Systems Become a Key Urban Technology

Hefei’s robotics deployment is part of a broader push to integrate artificial intelligence, sensors, and data analytics into public transport networks. Smart metro systems increasingly rely on predictive maintenance, real-time monitoring, and automated inspection technologies to maintain reliability and reduce disruptions.

Data collected by robots and sensors can be analyzed to identify early signs of equipment wear or operational anomalies, allowing maintenance teams to address problems before they lead to service interruptions.

These capabilities are becoming increasingly important as urban rail networks expand and passenger demand continues to grow.

Cities around the world are exploring similar technologies as part of broader smart city initiatives designed to improve infrastructure efficiency and safety.

Robotics Moves into Everyday Urban Infrastructure

The Hefei Metro project highlights how robotics is gradually becoming embedded in everyday public systems rather than remaining confined to industrial environments. From transportation and utilities to construction and maintenance, robots are increasingly being used to monitor and manage complex infrastructure.

For urban transit networks, robotics offers the ability to improve safety while reducing operational costs and manual inspection requirements.

Hefei’s deployment provides an early example of how multiple types of robots – including quadrupeds, drones, and humanoids – can work together within a coordinated infrastructure system.

As cities continue to modernize their transportation networks, robotics and AI are likely to play a growing role in ensuring that critical infrastructure remains safe, efficient, and capable of supporting expanding urban populations.

Siemens has partnered with Expert Technologies Group and RMGroup to create the United Kingdom’s first manufacturing capability dedicated to fully customizable autonomous mobile robots (AMRs). The initiative aims to provide domestic manufacturers with flexible automation systems designed to improve factory logistics and material handling.

The collaboration marks a strategic effort to strengthen the UK’s industrial automation ecosystem by developing robotics systems locally rather than relying on imported technologies. By combining Siemens’ automation software with robotics integration expertise from the two partner companies, the project creates an end-to-end platform for deploying AMR systems in manufacturing and warehouse environments.

Autonomous mobile robots are increasingly used to move materials within factories and distribution centers, offering an alternative to traditional automated guided vehicles that rely on fixed tracks or dedicated infrastructure.

Flexible Robotics for Modern Manufacturing

AMRs differ from earlier factory transport systems by navigating environments using onboard sensors, real-time mapping, and intelligent path planning. This allows them to move dynamically through busy industrial settings while avoiding obstacles and adapting to layout changes.

The robots produced through the new partnership are designed to be customizable for different industrial environments. Manufacturers can configure systems to support tasks such as delivering components to assembly lines, transporting finished goods to storage areas, or supplying materials to production cells.

The technology platform integrates Siemens’ SIMOVE control software, which enables robots to coordinate movements and manage logistics operations across facilities. Expert Technologies Group contributes its FlexDrive AMR platform, providing modular drive systems and navigation capabilities. RMGroup adds robotics integration and safety systems designed for industrial environments.

Together, these technologies allow factories to deploy robotics fleets tailored to their operational requirements, rather than adapting workflows to standardized equipment.

Building Domestic Robotics Capability

The partnership reflects growing interest in strengthening domestic robotics manufacturing in the UK. Many automation systems used in British factories are imported, which can create challenges related to integration, maintenance, and technical support.

By building robots locally and supporting them with domestic engineering teams, the collaboration aims to provide manufacturers with more reliable deployment and long-term support. The partners say this approach addresses a common problem in automation projects where overseas suppliers cannot provide sufficient integration support.

Local development also allows robotics systems to be adapted more easily to specific industry requirements, including aerospace, automotive, food processing, and logistics operations.

In addition to robotics hardware, the system incorporates wireless connectivity technologies such as industrial Wi-Fi and 5G to support real-time communication between robots, factory infrastructure, and digital management systems.

Autonomous Logistics Becomes Central to Industrial Automation

Material movement inside factories and warehouses represents a significant operational challenge for manufacturers. Traditionally, these tasks rely heavily on manual labor or fixed automation systems that are difficult to modify when production changes.

Autonomous mobile robots offer a more adaptable solution by enabling dynamic logistics operations. Fleets of AMRs can coordinate movements, optimize routes, and respond to changing workloads without requiring major infrastructure modifications.

These capabilities also allow manufacturers to scale automation gradually, starting with small deployments and expanding fleets as operational needs grow.

As manufacturing becomes more digital and data-driven, AMRs are increasingly integrated with digital twin simulations and factory management systems. This allows companies to analyze workflows, optimize operations, and improve productivity.

The Siemens-led collaboration represents a step toward building a domestic robotics ecosystem capable of supporting these advanced manufacturing technologies. By combining automation software, robotics hardware, and integration expertise, the partnership aims to help UK manufacturers deploy flexible, intelligent logistics systems as automation becomes an essential component of modern industrial production.