# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 8 Conclusions  
In this work, we presented an approach to improving the positioning accuracy of autonomous ship hull-cleaning robots operating in underwater environments. By combining classical kinematic models with modern sensor data processing techniques, we have demonstrated the potential to mitigate the cumulative errors inherent to odometry-based positioning, particularly those caused by wheel slippage.  
Our experimental evaluations on the Rosario dataset and in simulation environments have shown that the Gradient Boosting models can correct the displacement and heading errors that arise from pure odometry estimates. Compared to the uncorrected odometry trajectory, both the Gradient Boosting and deep learning approaches yielded improved alignment with the GPS reference route. The deep learning model, using sequential LSTM layers, showed promising results in forecasting both displacement vectors and rotation angles over varying GPS-based correction intervals. Although prediction errors accumulate over time, the ability to periodically correct the trajectory with GPS data resulted in an overall reduction of the accumulated error. Our experiments also highlighted the importance of periodic position corrections, with different correction frequencies revealing trade-offs between computational complexity and positioning accuracy.  
We also presented a slip detection and correction module that improved the robot's ability to maintain accurate positioning despite wheel slippage. The final positioning algorithm, which incorporated preprocessing, slip removal, and sensor fusion, achieved high accuracy with minimal deviations from the reference trajectories in both simulated and real-world tests.  
Future work will focus on further validation in real-world maritime environments and the exploration of additional sensor modalities to enhance positioning accuracy and reliability.  
Conceptualization, L.W. and M.M.; methodology, L.W. and K.C.; validation, P.S.; software, A.K.; investigation, M.M.; data curation P.S.; resources, P.S.; writing--original draft preparation, L.W. and M.M.; writing--review and editing, L.W., M.M., K.C., P.S. and A.K.; visualization, M.M.; supervision, M.S.; project administration, M.S.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.  
Figure 21: The route determined using the proposed algorithm.  
**Funding:** The work was financed within the project \"An effective and innovative system for working on ferromagnetic surfaces using pressurized water\". (POIR.01.01.01-00-0863/18).  
**Data Availability Statement:** The data are available at [[https://github.com/adaa-polsl/ship-hull-cleaning-robot-positioning](https://github.com/adaa-polsl/ship-hull-cleaning-robot-positioning)]([https://github.com/adaa-polsl/ship-hull-cleaning-robot-positioning](https://github.com/adaa-polsl/ship-hull-cleaning-robot-positioning)) (accessed on 15 May 2025).  
**Conflicts of Interest:** Author [PERSON] was employed by the company SR Robotics Sp. z o.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.