Austrian University 🇦🇹

Austrian University 🇦🇹

Note: We have written a dedicated white paper for this case. Download here for free.


Case Study: Implementing Microbots at an Austrian University


The adoption of microbots, specifically the Robot Sweeper 8, at an Austrian university represents a significant advancement in automating maintenance tasks within educational institutions. This case study outlines the challenges, setup, implementation, and results of integrating these robots, providing insights and learnings beneficial for other universities considering similar automation solutions.

The Challenge

Universities face unique challenges in maintaining cleanliness, particularly in high-traffic areas like corridors. The primary goal of this project was to automate the cleaning of long corridors, thereby reducing the workload of the cleaning staff and ensuring uninterrupted academic activities. The project was launched in Q1 2024, in collaboration with Strabag PFS and dealer partner Lloyd, deploying 20 Robot Sweeper 8 units.

Initial Setup and Implementation


  • Site Preparation: The university did not previously use robots from other manufacturers. They decided that the Robot Sweeper 8 was the right choice for them. This decision was driven by the sweeper’s efficiency on short-pile carpets, compatibility with narrow corridors, and its low cost and maintenance. 
  • Network Integration: A significant challenge was integrating the robots into the university’s network, overcoming issues like missing certificates or firewall settings. All of these minor hiccups were promptly fixed by the FieldBots team and the University personnel. 
  • Technical Preparations: Ensuring accessible Wi-Fi and having a technical contact on standby were critical steps. Detailed building plans were used to predefine installation locations, ensuring no emergency exits were blocked.

Setup Phase


  • Monitoring and Optimization: Continuous monitoring during the initial phase was crucial. Feedback from robots helped in creating virtual walls and no-go areas to prevent navigation issues. Certain areas can cause the Microbots to get stuck or tangled, so establishing areas with cables and sensitive equipment as no-go zones plays a key role.
  • Defined Responsibilities: To ensure a smooth kickoff, there was a clearly defined setup phase, that extended 2 weeks post-launch, with a designated person responsible for optimization.


Special Considerations for Educational Institutions


  • Operational Hours: Cleaning operations were scheduled at night to avoid disrupting students and staff. This same consideration could be also applied to most other workspaces that have the bulk of their foot-traffic during the day. The robots don’t need any external light source, so they can easily run in the dark. 
  • Privacy and Safety: The Robot Sweeper 8 is equipped with LiDAR instead of cameras, ensuring privacy and data protection. Theft protection features were activated, and stickers were placed on the devices to deter vandalism. We are happy that six months after the launch we’ve received 0 reports of theft or vandalism.
  • Space Suitability: The focus was on cleaning short corridors due to limitations posed by stairs and low furniture in other areas.


Learnings and Results

Scheduling Efficiency


Smart Scheduling: The use of split maps allowed robots to clean different sections of the corridor on alternate nights, optimizing battery usage and reducing hardware costs.

Theft and Vandalism


Minimal Issues: Contrary to initial concerns, theft and vandalism were not significant issues. Minor incidents, like students manually activating robots or gluing eyes on them, were quickly resolved. And in the case of the googly eyes, we decided to leave them on, because they gave the Robot Sweeper 8 a bit more personality.

Performance Monitoring


Efficiency Metrics: FieldBots OS provided comprehensive performance data, enabling continuous optimization of maps, schedules, and installation locations to enhance the fleet’s efficiency.



The deployment of microbots at the Austrian university demonstrated significant potential for automating cleaning tasks in semi-public spaces. Key factors contributing to the success included effective network integration, clear communication of setup phases, and continuous optimization of robot operations. The Robot Sweeper 8 proved to be a cost-effective, reliable solution familiar to users, thereby facilitating its acceptance and integration within the university environment.

If you would like to discuss your particular use case whether it be on the robot side, or the software side, you can schedule a call with

Note: We have written a dedicated white paper for this case. Download here for free.

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