Using the Global Positioning System (GPS) and the mobility of marine surface vehicles, this paper addresses the navigation problem between unmanned surface vehicles (USVs) and autonomous underwater vehicles (AUVs). We propose a moving AUV state estimation method based on the trajectory optimization of the USV. In particular, by exploring the Doppler effect on the frequency of arrival (FOA) of the acoustic signals received by a single-surface USV, the position and velocity of the AUV can be estimated simultaneously, offering a robust solution that eliminates the need for time synchronization. Moreover, the USV trajectory is dynamically adjusted to achieve optimal USV–AUV measurement geometry, thereby improving the AUV’s observability and enhancing state estimation performance. The innovation lies in a tailored cost function grounded in observability analysis via the Cramér–Rao lower bound (CRLB) and geometric constraints. It integrates (1) the CRLB to optimize system observability, thereby enhancing estimation accuracy, (2) a distance term to ensure that the USV maintains appropriate proximity to the AUV, and (3) a turning rate term that adjusts the USV’s orientation to improve following capability. The cost function is then minimized using a particle swarm optimization algorithm, balancing these components to achieve a robust AUV tracking framework. We conduct comprehensive simulations to examine the potential influences of different factors, including the complexity of the USV trajectory, AUV depth, measurement frequency, packet loss rate, and noise levels, on navigation performance. Simulation results demonstrate the effectiveness of the proposed method in estimating and tracking the AUV.

In this paper, the scheduling of yard cranes (YCs) and external trucks (ETs) working in a U-shaped automated container terminal yard is proposed as a new problem. This problem arises due to the characteristic of the U-shaped layout, where the ETs enter the yard through the U-shaped lanes and interact with the YCs. To formulate this problem, a three-objective optimization model is established to simultaneously schedule YCs and ETs, considering their efficiencies. Its solution is based on the nondominated sorting genetic algorithm III (NSGA-III), which is improved during initialization, crossover and mutation to make it applicable to the problem. To calculate the objective values in each iteration of the NSGA-III, a method to make the equipment interactions equal at all times is proposed. As there are special requirements for the priority of the objectives, a new method of selecting the final solution is presented, which leads to a more suitable solution. The case study involving sensitivity analysis shows that appropriate equipment quantity arrangement and task assignments have a positive impact on improving the efficiency of yard operations. Experimental results demonstrate that the proposed model and algorithms perform better. In summary, the model and algorithms proposed in this paper can effectively solve the proposed problem, which can offer a variety of options for decision-makers in an actual terminal operation environment.Note to Practitioners—The YC scheduling of a U-shaped layout presents new characteristics, and the influence of ETs cannot be ignored, posing a novel problem. Furthermore, during the operation of container terminals, extended waiting time for ETs is often caused by considerations related to ship schedules, especially in the mixed stacking mode. This can frustrate cargo owners and waste valuable ET resources. Hence, this paper designs a joint scheduling strategy for YCs and ETs in U-shaped yard layouts. The proposed strategy combines optimizing YC operation plans with maximizing ET efficiency, while ensuring efficient yard operations.

Autonomous underwater vehicles (AUVs) are widely used in sampling on-site the seawater parameters, such as temperature, salinity and biomass for better understanding the ocean. The AUV path needs to be carefully planned in order to maximize the sampled information within the power constraints, which is known as the informative path planning (IPP). The existence of ocean currents further complicates the problem. This article proposes an IPP method for AUVs under the influence of ocean currents via combining the probabilistic roadmap and$Q$-learning. Specifically, the$Q$-learning algorithm builds an informative optimal AUV path by traversing a learned and updated$Q$-table. The$Q$-value in the table represents the expectation of the obtained reward if taking a certain action moving from one position to another. Considering the characteristics of the IPP task, we design the reward matrix in$Q$-learning using the prior knowledge on the environment information. A convergent$Q$-table guarantees that only one complete training and learning is required to generate the path between any two positions. This feature facilitates converting the possible repetitive path plannings into simple search problems, and thus the automatic return is easily realized whenever the AUV residual energy is insufficient. Moreover, to improve the efficiency of the$Q$-learning algorithm, a probabilistic roadmap with random sampling is generated and combined with the$Q$-learning. Various simulations and comparisons are carried out. The results demonstrate the effectiveness of the proposed IPP algorithm, showing that the convergence of the path planning can be achieved quickly and successfully. The superiority in terms of efficient return path planning over the traditional path planning method, RRT*, is also demonstrated.

The trajectory tracking control of unmanned surface vehicles (USVs) generally faces challenges from complex uncertain hydrodynamics and system constraints. In this paper, we leverage the capability of the Deep Neural Networks (DNN) in approximating arbitrary nonlinear dynamics, and propose a hybrid physics-learning model based predictive control (PL-MPC) method, Neural-sailing, for USVs. The first feature of PL-MPC is robust in that it handles the modeling errors caused by either the USV uncertain hydrodynamics or the environmental disturbances. Particularly, the hybrid physics-learning model combines a conventional mass-Coriolis-damping USV maneuvering model and a DNN model. The DNN model is trained with comprehensive USV motion data. The hybrid model enables efficient model-based control design with easily pre-obtained simple models. Moreover, the stability of the PL-MPC is guaranteed following the quasi-infinite nonlinear predictive control scheme. The second feature of PL-MPC is being computationally efficient. We propose a successive linearization approach to deal with the complexity of the prediction model caused by the introduction of DNN. The optimal control sequence computed from the previous step is utilized to obtain a linearization trajectory in the current step. This is the first time that a hybrid physics-learning model is successively linearized and used for predictive control. Simulation results reveal that PL-MPC has higher tracking precision than the conventional predictive control. The PL-MPC with successive linearizations has comparable tracking control performance with that of PL-MPC. However, with successive linearizations, the controller is much more computationally efficient, and achieves the trade-off between the control performance and computational burden.

In the underwater medium, the speed of sound varies with water depth, temperature, and salinity. The inhomogeneity of water leads to bending of sound rays, making the existing localization algorithms based on straight-line propagation less precise. To realize high-precision node positioning in underwater acoustic sensor networks (UASNs), a multi-layer isogradient sound speed profile (SSP) model is developed using the linear segmentation approximation approach. Then, the sound ray tracking problem is converted into a polynomial root-searching problem. Based on the derived gradient of the signal’s Doppler shift at the sensor node, a novel underwater node localization algorithm is proposed using both the time difference of arrival (TDOA) and frequency difference of arrival (FDOA). Simulations are implemented to illustrate the effectiveness of the proposed algorithm. Compared with the traditional straight-line propagation method, the proposed algorithm can effectively handle the sound ray bending phenomenon. Estimation accuracy with different SSP modeling errors is also investigated. Overall, accurate and reliable node localization can be achieved.

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

This article considers intelligent fuel cell/battery hybrid vehicles (FCHVs) that can make autonomous decisions at both the vehicle and powertrain levels. Since the vehicle and powertrain level dynamics are inherently integrated, we propose an integrated motion and powertrain model predictive control approach for intelligent FCHVs by jointly optimizing the vehicle acceleration and fuel cell current. The control goals are to achieve vehicle mobility, minimal hydrogen consumption, and battery state-of-charge maintenance within system constraints. The main challenge in an integrated control is that the electric motor can operate in both propelling and generating modes coupling with vehicle and powertrain states. This hybrid operation is handled by the mixed logical dynamical modeling resulting in a mixed integer nonlinear control problem. To relieve the possible heavy computational burden, two simplification approaches are proposed: hierarchical control and successive linearizations. Two standard driving cycles and a typical vehicle cruising scenario are employed to test the effectiveness of the proposed modeling and control algorithms. Simulation results show that the hierarchical linear control is more suitable for real-time applications with comparable control performance with that of the integrated control. However, additional constraints must be carefully designed to compensate for the ignored coupling dynamics and constraints.

Urban waterways have great potential in cargo transport to relieve the congestion in the overloaded road networks. This paper explores the potential of applying cooperative multi-vessel systems (CMVSs) to improve the safety and efficiency of transport in urban waterway networks. A framework consisting of vessel train formation (VTF) and cooperative waterway intersection scheduling (CWIS) is proposed. Two types of controllers are introduced. Intersection controllers solve the CWIS problems and assign each vessel a desired time of arrival and vessel controllers are responsible for the VTF in waterway segments and the timely arrival at the intersections. An alternating direction method of multipliers (ADMM)-based negotiation framework is proposed for the cooperation among the controllers. The simulation experiments involving the scenarios in which up to 50 vessels sailing in the canal network in Amsterdam are carried out to illustrate the effectiveness of the proposed approach. In the simulation of an isolated intersection, rescheduling is triggered when some vessels cannot arrive on time. Although some ASVs arrive later, the time that is needed for all the ASVs to pass through is the same after rescheduling. Moreover, we compare the cooperative situation with the proposed CMVSs with a baseline situation. In the baseline situation, vessels avoid collisions using the generalized velocity obstacle (GVO) method and cross the intersection with a first in, first out rule. The CMVSs show better path following performance, while the GVO method needs fewer velocity changes. From the perspective of efficiency, the CMVSs help to reduce the total time to pass through the intersection.

Waterborne autonomous guided vessels (waterborne AGVs) moving over open waters experience environmental uncertainties. This paper proposes a novel cost-effective robust distributed control approach for waterborne AGVs. The overall system is uncertain and has independent subsystem dynamics but coupling objectives and state constraints. Waterborne AGVs determine their actions in a parallel way, while still minimizing an overall cost function and respecting coupling constraints robustly by communicating within a neighborhood. Our first contribution is the proposal of the system robustness level for the cost-effective robust distributed model predictive control (RDMPC) for waterborne AGVs. Cost-effective RDMPC models the price of robustness by explicitly considering uncertainty and system characteristics in a tube-based robust control framework. The second contribution is an efficient integrated branch & bound (B&B) and the alternating direction method of multipliers (ADMMs) algorithm for solving the cost-effective RDMPC problem. The algorithm exploits special ordered variable sets and combining branching criteria with intermediate ADMM results conducting smart search in B&B. Simulation results demonstrate the effectiveness of the proposed approach for cooperative distributed waterborne AGVs with cost-effective robustness.

Transport over water plays an important role in the transport of goods in Europe. More than 37,000 kilometers of waterways connect hundreds of cities and industrial regions. Nowadays, the European Commission aims to promote and strengthen the competitive position of transport over water in the transport system and to facilitate its integration into the intermodal logistic chain due to its reliability, low environmental impact and ample capacity available for increased exploitation. The objective of this paper is to propose a new integrated framework for improving the efficiency of transport over water at both tactical and operational levels. Research directions addressing main problems at these levels are discussed in this paper: vessel rotation planning and path following for waterborne AGVs. Moreover, this paper presents perspectives regarding future research directions to develop this integrated framework.