Autonomous Underwater Vehicles (AUVs) encounter significant challenges in confined spaces like ports and testing tanks, where vehicle-environment interactions, such as wave reflections and unsteady flows, introduce complex, time-varying disturbances. Model-based state estimation methods can struggle to handle these dynamics, leading to localization errors. To address this, we propose a data-driven velocity estimation approach using Inertial Measurement Units (IMUs) and a Gated Recurrent Unit (GRU) neural network, capturing temporal dependencies and rejecting external disturbances. This velocity estimator is then integrated into a sensor fusion framework using an asynchronous Kalman filter to improve localization by fusing on-board and off-board sensor information. Experimental validation on miniature AUVs demonstrates the effectiveness of the proposed method in enhancing accuracy for velocity and position estimation in environments with significant disturbances due to interactions between the vehicle and the environment.

Long propagation delay of acoustic signal has long been considered the nemesis of wireless underwater communication. This paper proposes a novel space-propagation-aware Medium Access Control (MAC) protocol for swarm of Autonomous Underwater Vehicles (AUV). We show that, with proper design, we can take advantage of propagation delay, and improve underwater channel utilization in a way that is not possible in terrestrial wireless networks. The proposed MAC protocol leverages a unique spatial-temporal property of acoustic medium, enabling a fully decentralized channel access for multiple underwater nodes, while maintaining zero packet collisions at high traffic. The results from simulations and real experiments show that the protocol outperforms contention-based, and contention-free channel access schemes in terms of scalability, and energy consumption, achieving up to 50% gain in network throughput. In addition, the proposed scheme does not require prior knowledge about network topology, and it can successfully operate in highly dynamic networks without synchronization overhead. This makes the protocol a practical, and efficient solution for densely deployed underwater networks, such as swarm of AUVs.

Opinions of individuals in large groups evolve through interactions with neighbors and the environment, which can be modeled with opinion dynamics. In this paper, we propose a distributed opinion-based strategy for large-scale multi-robot task allocation utilizing the convergence behaviors of opinion dynamics. The strategy relies on the specialized opinion dynamics on the unit sphere for robot task selection. We investigate the convergence behaviors of opinion dynamics in the context of regions of attraction. Simulation results with a swarm of 200 homogeneous robots validate the effectiveness of our proposed strategy.