Trustworthy Load Prediction for Cantilever Roadheader Robot Without Imputation

We propose a trustworthy load prediction method for a cantilever roadheader robot without imputation. Specifically, we design a load-trustworthy-boosting (LTB) algorithm for coal and rock cutting loads that accounts for missing data in complex underground environments. We introduce a trustworthy decision tree that integrates mixed-integer programming (MIP) and Missing Incorporated in Attributes (MIA) as the base predictor, which can handle missing data, thereby accelerating load prediction and improving prediction accuracy. Furthermore, we utilize boosting techniques to enhance the prediction performance of the base predictor by incorporating cutting safety–trust constraints during the prediction process. We derive the convergence of the algorithm theoretically and verify the accuracy and reliability of the algorithm through experiments. The experimental results show that the proposed algorithm is superior to state-of-the-art load prediction algorithms both without and with missing data considered. This method can provide a reliable decision-making basis for underground unmanned intelligent excavation.