Azimuth thrusters are widely used for controlling marine vehicles, especially, for dynamic positioning and hovering purposes. However, including azimuth thruster makes control allocation a nonlinear non-convex problem which is commonly solved using nonlinear programming methods, simplified by paring azimuth thrusters (e.g., two thrusters will always move at the same angle), or locally linearized using approximation equations such as Taylor series expansions and polynomial functions. In this paper, a new approach is presented to modify the azimuth thruster control allocation problem into a convex quadratic problem with a new force decomposition and linear first-order inequality constraints. As a result, the complexity of the control allocation increases linearly with respect to the number of azimuth thrusters, allowing it to be implemented on the marine vehicles with increased numbers of azimuth thrusters controlled independently and can be solved using constrained quadratic programming solvers. Case studies has been presented to validate the proposed method on simulated Autonomous Underwater Vehicles (AUVs) with two and four azimuth thrusters configured with different azimuth angle limits (±45, ±90, and ±135 degrees). The results shows excellent control performance of the proposed approach in controlling multiple states (surge, pitch, yaw, depth and sway) simultaneously, even when experiencing a cross-track ocean current. Recommendation on hardware implementation is also discussed for real world platform integration.

The use of chatbots in digital public services is becoming increasingly widespread, and therefore, it is necessary to foster citizens’ trust in chatbots to enhance their satisfaction with such services. Although empathy is recognised as a critical component of public service interactions, research regarding empathetic chatbot communication in the public sector remains limited. This study applies the Stimulus-Organism-Response (SOR) framework to explore the mechanisms and boundary conditions of empathetic chatbot expressions. The findings from a scenario-based online experiment involving 297 Chinese participants demonstrate that empathetic expressions significantly enhance both affective and cognitive trust, which further boost users’ satisfaction with digital public services. Notably, previous experience with chatbots and the anthropomorphism of the chatbot identity moderate the effectiveness of empathetic expressions. Overall, this study highlights the advantages and boundary conditions of empathetic expressions in public service chatbots, thereby providing valuable insights for their design and application in the context of digital public services.

Underwater robots, like Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for the exploration and study of the under-ice environment and the ecosystems that thrive there. However, state estimation is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper,$w$e present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated using a Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides, a modified keyframe-based clone marginalization and a new DVL-aided feature enhancement method are presented to further improve the localization performance. The proposed method is validated in the under-ice environment on Lake Michigan, USA, and the results are cross-compared with 10 other different sensor fusion setups. Overall, the integration of keyframe enabled and DVL-aided feature enhancement yielded the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path over a total traveling distance of about 200 m.

An adaptive sensing method is presented to control the pinging interval of a downward-looking sonar on an Autonomous Underwater Vehicle. The goal is to conserve energy via adjusting the pinging rate automatically without reducing the localization accuracy when using terrain-aided navigation (TAN). In this paper, the TAN is implemented using a particle filter and a bias velocity estimator developed based on a Kalman filter. The adaptation on the sonar pinging interval is determined based on the depth variation of local seafloor topography which is quantified using a modified Teager Kaiser energy operator. As a result, more measurements are collected on high relief regions, and less measurements are obtained on relatively flat and smooth regions. We evaluated the adaptive sensing method in a simulated environment and applied it to a field data set. The results show that the adaptive sensing method produces an improved navigational accuracy compared to the missions with fixed sonar pinging rates. In the offline field missions, the energy consumed by the altimeter is reduced to about 30% in the adaptive sensing missions compared to continuously sensing missions where the altimeter is pinging consistently without switching off.

It is important to register oceanographic data into a geo-referenced coordinate system. Knowing the location of the sampling is critical. For a marine robotic network, the location of Unmanned Surface Vessels (USVs) can be measured using a Global Positioning System (GPS), however the navigation of Autonomous Underwater Vehicles (AUVs) is more challenging. In this paper, we present a method for determining the position of underwater vehicles from a moving USV using the relative range information provided by an Ultra-Short Baseline (USBL)/acoustic modem. The navigation method uses an Extended Kalman Filter (EKF) to update the states predicted from a model-based dead-reckoning technique. Since the vehicle model is relative to the surrounding fluid, we have introduced two environmental states in the state matrix. Such a modification allows us to quantify the effects induced by the ocean current on the vehicle's speed. Beyond that, the method uses a limited number of sensors, only attitude sensors and an USBL/acoustic modem, offering an alternative for AUVs without expensive instruments such as a Doppler Velocity Log (DVL) and an Inertial Measurement Unit (IMU). Experiments are conducted to evaluate the range-based navigation method on a hybrid Slocum underwater glider with an USV. As a result from the reference trial, the estimated glider position stays within the error of 15 meters comparing to the measured position of a surface buoy where the glider is attached. In the open-water trial, trajectories estimated from the range-based navigation are compared with dead-reckoning paths and current-compensated dead-reckoning paths (reference). As a result, the distance errors are bounded with the proposed navigation method while the dead-reckoning errors grow without bound.