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.

In this paper, we propose an approach for robust visual Simultaneous Localisation and Mapping (SLAM) in underwater environments leveraging acoustic, inertial and altimeter/depth sensors. Underwater visual SLAM is challenging due to factors including poor visibility caused by suspended particles in water, a lack of light and insufficient texture in the scene. Because of this, many state-of-the-art approaches rely on acoustic sensing instead of vision for underwater navigation.Building on the sparse visual SLAM system ORB-SLAM2, this paper proposes to improve the robustness of camera pose estimation in underwater environments by leveraging acoustic odometry, which derives a drifting estimate of the 6-DoF robot pose from fusion of a Doppler Velocity Log (DVL), a gyroscope and an altimeter or depth sensor. Acoustic odometry estimates are used as motion priors and we formulate pose residuals that are integrated within the camera pose tracking, local and global bundle adjustment procedures of ORB-SLAM2.The original design of ORB-SLAM2 supports a single map and it enters relocalisation when tracking is lost. This is a significant problem for scenarios where a robot does a continuous scanning motion without returning to a previously visited location. One of our main contributions is to enable the system to create a new map whenever it encounters a new scene where visual odometry can work. This new map is connected with its predecessor in a common graph using estimates from the proposed acoustic odometry. Experimental results on two underwater vehicles demonstrate the increased robustness of our approach compared to baseline ORB-SLAM2 in both controlled, uncontrolled and field environments.

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.