Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.

Shallow water environments pose daunting scenarios for the operation of Unmanned Underwater Vehicles (UUVs), due to significantly larger wave disturbances being present in comparison to a typical deep sea situation. Performing inspection and maintenance tasks at close quarters in these conditions requires reliable control methods robust to external disturbances, allowing accurate position and attitude control, an aspect which classical control methods are often lacking. Improved performance can be achieved through predictive control methods, however, these require accurate and time-efficient estimations of the hydrodynamic forces produced by the immediate ocean environment around the vehicle. Considering this, we present a low-order model for faster-than-real time estimation of the wave-induced hydrodynamic forces acting on a submerged vehicle in various sea state conditions. The model is thoroughly corroborated by experimental tests, performed using a Remotely Operated Vehicle (ROV) situated at shallow depth whilst subjected to realistic sea wave disturbances. Validation between simulations and the collected experimental data showed a maximum normalised mean error deviation of 0.16 and 0.27 for surge and heave forces respectively, and 0.34 for the pitching moment. This empirical evidence demonstrates that accurate predictions of wave-generated forces can be produced through low-order models at a speed suitable for incorporation within predictive control architectures.