If you are an asbestos removal contractor dealing with high labor costs, strict safety regulations, and worker health liability — this project developed a robotic system with mobile platforms and lightweight robotic arms that autonomously grinds contaminated paint from walls. The system was tested through multiple iterations (V1 and V2) at a real rehabilitation site, removing workers from direct asbestos exposure.
Robots That Remove Asbestos From Buildings So Workers Don't Have To
Imagine you need to strip asbestos paint off walls in an old building. Right now, workers in full hazmat suits do it by hand — slow, expensive, and dangerous. This project built robots that roll into the room, scan the walls with radar and optical sensors, and grind off the contaminated material automatically. Think of it like a Roomba, but instead of vacuuming your floor, it's scraping poison off walls while humans stay safely outside.
What needed solving
Asbestos removal from buildings is one of the most dangerous and expensive tasks in the construction industry. Workers must wear full protective equipment, follow strict containment procedures, and work slowly to minimize fiber release — all of which drives up costs dramatically. Despite decades of automation in manufacturing, the construction and demolition industry still relies almost entirely on manual labor for hazardous tasks like this.
What was built
A complete robotic system for automated asbestos removal, including: mobile platforms (V1 and V2), lightweight robotic arms with abrasive tools and aspiration (V1 and V2), radar and optical sensors for navigation in dusty conditions, a sensor system for local process monitoring, autonomous task planning and process control, and semi-autonomous execution of asbestos removal tasks like disk grinding contaminated paint from walls.
Who needs this
Who can put this to work
If you are a renovation company managing building rehabilitation projects with asbestos contamination — this project built a complete robotic system with autonomous task planning, environmental perception sensors, and process control that works even in dusty conditions. The 8-partner consortium tested the system at real rehabilitation sites across 4 countries.
If you are a robotics manufacturer looking for new verticals beyond factory floors — this project demonstrated that mobile robotic platforms with abrasive tools, radar sensors, and autonomous navigation can operate in unstructured construction environments. The consortium included 5 industry partners and 3 SMEs, signaling strong commercial interest in this application.
Quick answers
What would a robotic asbestos removal system cost compared to manual methods?
The project data does not include specific cost figures. However, manual asbestos removal requires extensive safety measures (containment, hazmat suits, decontamination) that make it extremely expensive per square meter. A robotic system eliminates most worker safety overhead and can operate continuously without breaks.
Can this system work at industrial scale on real buildings?
Yes — the project explicitly tested the robotic system V2 at a real rehabilitation site, not just in a lab. The system includes autonomous task planning and process control for operations like disk grinding of contaminated paint from walls. Multiple iterations (V1 and V2) of both the robotic arm and mobile platform were developed and tested.
What is the IP situation and how can I license this technology?
The project was coordinated by RWTH Aachen with 8 partners across 4 countries. IP rights are typically shared among consortium members under the Horizon 2020 grant agreement. With 5 industry partners and 3 SMEs in the consortium, commercial licensing arrangements would need to be negotiated with the relevant partners.
Does the system handle the dusty conditions typical of asbestos removal?
Yes — the project specifically developed radar and optical sensors for environmental perception and navigation that work even in dusty conditions. A dedicated sensor system for local process monitoring was implemented and tested as a separate deliverable.
How autonomous is the robot — does it still need human operators?
The system uses semi-autonomous operation with human supervision. Autonomous task planning, process control and execution were implemented, but the user still specifies and supervises tasks through a virtual representation of the site. This means fewer workers are needed, but skilled operators still oversee the process remotely.
What regulations does this comply with for asbestos work?
Based on available project data, the system was designed to meet the demanding safety requirements of asbestos removal in the EU construction and demolition industry. The project tested and validated operational processes at real rehabilitation sites. Specific regulatory certifications are not detailed in the available data.
How long before this could be deployed commercially?
The project ended in November 2019 with a demonstrated system prototype in an operational environment. The objective explicitly mentions later commercialisation of the system as a goal. Based on available project data, the technology would need additional engineering for full commercial deployment.
Who built it
This is a strong industry-driven consortium with 62% industry participation — 5 industry partners including 3 SMEs alongside 2 universities and 1 research organization. The 8 partners span 4 countries (Germany, Spain, France, Italy), all major European construction markets. RWTH Aachen, one of Europe's top technical universities, coordinates. The heavy industry presence and Innovation Action funding scheme signal this was built for commercialization, not just academic research. The mix of SMEs and larger industry players suggests both agile development capability and routes to market.
- RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHENCoordinator · DE
- ROBOTNIK AUTOMATION SLparticipant · ES
- DANIELI TELEROBOT LABS S.R.L.participant · IT
- CLERMONT AUVERGNE INPparticipant · FR
- BOUYGUES CONSTRUCTION SAparticipant · FR
- FUNDACIO EURECATparticipant · ES
RWTH Aachen coordinated the project — reach out through SciTransfer for a warm introduction to the project team.
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