Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments

This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stochastic ocean environments. First, the pre-planning tier solves the single-AUV routing problem to find the optimal giant route (GR), estimates the number of required AUVs based on GR segmentation, and allocates nodes for each AUV to access. Second, the route planning tier plans individual routes for each AUV. During navigation, the path planning tier provides each AUV with physical paths between any two points, while the actuation tier is responsible for path tracking and obstacle avoidance. Finally, in the stochastic ocean environment, deviations from the initial plan may occur, thus, an auction-based coordination tier drives online task coordination among AUVs in a distributed manner. Simulation experiments are conducted in multiple different scenarios to test the performance of the proposed planner, and the promising results show that the proposed method reduces AUV usage by 7.5% compared with the existing methods. When using the same number of AUVs, the fleet equipped with the proposed planner achieves a 6.2% improvement in average task completion rate

Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and corresponding losses are separated, so the weights between output parameters are not considered. Not all partial derivatives participate in gradient backpropagation, and the remaining terms will be reused.Therefore, compared with traditional PINN, this method is a rapid version. Here, labeled data, physical constraints and network outputs are regarded as priori information, and the residuals of the N-S equations are regarded as posteriori information. So this method can deal with both data-driven and data-free problems. As a result, it can solve the special form of compressible N-S equations -- -Shallow-Water equations, and incompressible N-S equations. As boundary conditions are known, this method only needs the flow field information at a certain time to restore the past and future flow field information. We solve the progress of a solitary wave onto a shelving beach and the dispersion of the hot water in the flow, which show this method's potential in the marine engineering. We also use incompressible equations with exact solutions to prove this method's correctness and universality. We find that PINN needs more strict boundary conditions to solve the N-S equation, because it has no computational boundary compared with the finite element method

Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous real-world scenarios, e.g., tracking a maneuvering underwater target with acoustic sensors. Simulation results demonstrate the effectiveness of the proposed filtering algorithm. In addition, considering the constraints of network energy resources, the issue of investigating a trade-off between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table

Rosin-Modified Polyurethane Elastomers with Room-Temperature Self-Healing Ability, High Strength, and Recyclability Based on Oxime Dynamic Bonds

Rosin, with a rigid hydrogenated phenanthrene ring, is a widely available biomass, but its high-value utilization needs to be enhanced. Inspired by sustainable development strategies, the design of polymer elastomers with a room-temperature self-healing capability has been a hot focus topic. However, designing elastomers that combine the conflicting properties of high mechanical performance and room-temperature self-healing is a significant challenge. The hydrogenated phenanthrene ring of rosin provides a superior solution to this problem. In this work, the polyurethane elastomer (BPU-X% AP) based on rosin-hydrogenated phenanthrene ring structure, dynamic oxime, and hydrogen bonding was reported. The BPU-X% AP exhibits high tensile strength (37.8 MPa), and good toughness (126.9 MJ m–3). Due to the rosin structure that promotes the movement of the elastomer chain segments, the elastomers have fast room-temperature self-healing and recyclability. Benefiting from their excellent mechanical strength and self-healing properties, BPU-X% AP as adhesives exhibits a strong lap shear strength of 5.5 MPa, and they can be used as hot melt binders. Corresponding to the cycling of the elastomer, the adhesive strength of BPU-10% AP remains almost the same after three cycles of adhesion to the iron and aluminum plates. This work provides a viable approach for the preparation of high-performance biomodified polyurethanes