Case Study: How to Solve the Problem of Slow Work of Window Cleaning Machine?

A large hotel chain with numerous glass windows and facades had recently invested in a fleet of window cleaning robots to streamline their cleaning operations.  However, they quickly encountered a significant issue: the robots were cleaning far too slowly, disrupting the hotel’s daily operations and requiring frequent manual intervention.  Frustrated with the delays and inefficiencies, the hotel’s management team reached out to us for a solution, hoping to optimize the robots' performance and get their cleaning process back on track.

The primary challenge was the robots' slow operation, which was far from the promised efficiency. The client provided feedback indicating that the robots took significantly longer to clean windows than expected. Our task was to identify the root cause of the problem and implement a solution that would enhance the robots' performance without compromising on cleaning quality.

Through a comprehensive analysis and subsequent adjustments, we were able to significantly improve the cleaning speed of the robots. The enhanced performance not only met but exceeded the client's expectations, restoring confidence in the automation process and reducing the need for manual labor.  Check out our custom products

1. Initial Diagnosis and Analysis: We began by thoroughly analyzing the client's existing setup. This involved reviewing the robots' operating parameters, software settings, and the physical condition of the window surfaces they were cleaning.
2. Software Optimization: Our engineers identified that the default cleaning path algorithm was inefficient for the type of glass used in the hotel. We optimized the software to streamline the cleaning process, allowing the robots to cover more area in less time while maintaining thoroughness.
3. Hardware Adjustments: Alongside software tweaks, we also made minor hardware adjustments to the robots. These modifications improved the grip and movement speed on the glass surface, further contributing to the reduction in cleaning time.
4. Testing and Iteration: We conducted multiple rounds of testing in a controlled environment, adjusting the robots' parameters based on real-time performance data. After several iterations, we achieved a balance between speed and quality that aligned with the hotel's requirements.
5. Implementation and Training: Once the adjustments were finalized, we implemented the changes across the entire fleet of robots. Additionally, we provided training to the hotel staff to ensure they were fully equipped to manage and maintain the optimized robots.
6. Ongoing Support: To ensure sustained performance, we offered ongoing technical support and periodic maintenance checks, guaranteeing the robots continued to operate at peak efficiency.