Enhancement of Jaibot: Developing Safety and Monitoring Features for Jaibot Using IoT Technologies

Abstract

The Hilti Jaibot, a state-of-the-art construction site drilling robot, has demonstrated remarkable productivity gains while also underscoring the need for improved safety and monitoring capabilities. This study aims to address this need by harnessing Internet of Things (IoT) technologies and predictive maintenance methodologies. The proposed enhancements encompass a comprehensive sensor and camera integration to monitor the robot's environment, coupled with the development of a Long Short-Term Memory (LSTM) predictive maintenance algorithm to preemptively identify operational issues. These improvements enable the Jaibot to autonomously detect and mitigate risks, such as obstacles and human activity, while providing real-time safety alerts to operators. Incorporating quantitative results from our predictive model, which successfully predicts three output variables (X, Y, and Z) using three input variables, we observed varying RMSE and MAPE values. Specifically, X exhibited an RMSE of 77.80% and a MAPE of 242.20%, while Y showed an RMSE of 31.10% and a MAPE of 69.70%, and Z had an RMSE of 34.53% and a MAPE of 82.74%. Notably, Y and Z data displayed high MAPE values, potentially attributed to data inconsistency. To enhance accuracy in our predictive model, we propose the utilization of more complex models and increased data volumes, which may mitigate the observed inconsistencies and lead to improved overall model performance. These findings from our quantitative analysis provide valuable insights for the integration of predictive maintenance algorithms into the Hilti Jaibot and lay the foundation for future advancements in robotic construction, emphasizing the pivotal role of IoT technology and predictive maintenance in shaping the industry's trajectory.