In this paper, we propose a novel cross-platform fault-tolerant surfacing controller for underwater robots, based on reinforcement learning (RL). Unlike conventional approaches, which require explicit identification of malfunctioning actuators, our method allows the robot to surface using only the remaining operational actuators without needing to pinpoint the failures. The proposed controller learns a robust policy capable of handling diverse failure scenarios across different actuator configurations. Moreover, we introduce a transfer learning mechanism that shares a part of the control policy across various underwater robots with different actuators, thus improving learning efficiency and generalization across platforms. To validate our approach, we conduct simulations on three different types of underwater robots: a hoveringtype AUV, a torpedo shaped AUV, and a turtle-shaped robot (U-CAT). Additionally, real-world experiments are performed, successfully transferring the learned policy from simulation to a physical U-CAT in a controlled environment. Our RLbased controller demonstrates superior performance in terms of stability and success rate compared to a baseline controller, achieving an 85.7 percent success rate in real-world tests compared to$\mathbf{5 7. 1}$percent with a baseline controller. This research provides a scalable and efficient solution for fault-tolerant control for diverse underwater platforms, with potential applications in real-world aquatic missions.

This work explores the use of artificial whiskers as tactile sensors for enhancing the perception and navigation capabilities of mobile robots in challenging settings such as caves and underground mines. These environments exhibit inconsistent lighting conditions, locally self-similar textures, and general poor visibility conditions, that can cause the performance of state-of-the-art vision-based methods to decline. In order to evaluate the efficacy of tactile sensing in this context, three algorithms were developed and tested with simulated and physical experiments: a wall-follower, a navigation algorithm based on Theta*, and a hybrid approach that combines the two. The obtained results highlight the efficacy of tactile sensing for wall-following in intricate environments. When paired with an external method for pose estimation, it further aids in navigating unknown environments. Moreover, by integrating navigation with wall-following, the third, hybrid algorithm enhanced the map traversal speed by roughly 26−43% compared to standard navigation methods without wall-following.

Classification of river morphology is often based on hydromorphological units (HMUs) identified from field measurements. Established survey methods rely on expert judgment or collection of field point measurements. When used for HMU classification, these methods can suffer from high errors due to the variations in the sampling environment, causing low repeatability. In order to expedite field data collection and increase HMU classification accuracy, we propose a multisensory device, the hydromast. Each hydromast provides a new source of data to classify HMUs. The modules are inexpensive and highly portable, consisting of a synchronous array of commodity pressure and inertial sensors. Rapid, local changes in the flow field are recorded with absolute and differential pressure sensors. At the same time, slower depth-integrated flow signals are obtained from a small damped cylindrical mast, driven by vortex-induced vibrations. In contrast to existing passive flow measurement technologies, the hydromast uses fluid-body interactions to provide flow measurements. This allows for minimal signal processing and simple feature extraction. An array of three hydromasts was used to collect ten samples in three river HMUs with shallow depths and highly turbulent flows with smooth and rough beds. We investigated classification accuracy using single, dual, and triple hydromast arrays with pressure, inertial, and combined features using linear regression, a genetic algorithm, and a neural network. Although limited in scope, the set of spot measurements covering three HMUs showed that a single multimodal sensor could deliver an overall classification accuracy of 89% of the HMUs, and an increase of up to 99% was achieved using a multimodal triple hydromast array. These preliminary results show promise in using hydromasts for rapid and robust HMU classification, providing a new way to collect and assess river survey data.