Surface Vehicle Research Articles

Acoustic mapping reveals macroalgal settlement following a retreating glacier front in the High Arctic

Vegetated coastal marine ecosystems are projected to expand northwards in the Arctic due to climate change, but the mechanisms for this expansion are complex and nuanced. Macroalgal biomass in the littoral areas of Svalbard has been increasing, but data at the glacier fronts are very scarce. In this study, we use hydroacoustics and video validation from an unmanned surface vehicle to survey macroalgal bed distribution along the coast of a High Arctic fjord (Billefjorden, Svalbard), including river bays and land- and sea- terminating glacier fronts, as well as oceanographic measurements to indicate physical drivers of macroalgal settlement. We found high variation of macroalgal coverage along the fjord coastline, with virtually no macroalgae in the river bays but abundant coverage in areas with little terrestrial runoff. Furthermore, the presence of kelp was found at the land-terminating glacier front which has recently retreated from the sea, which suggests the potential for rapid macroalgal establishment in newly available substrate following glacial retreat. These findings suggest large ecological implications throughout the Arctic, in which macroalgal expansion may lead to significant changes in the underwater coastal landscape and ecosystem. This study shows that the use of remote autonomous vehicles and hydroacoustic mapping with video validation has a high potential for sustainable and efficient ecological monitoring.

Characterisation of the Rigid Body Vibration Spectra of Road Transport Vehicles

ABSTRACTFor decades, numerous attempts at re‐creating realistic (vertical) vibrations that purport to represent typical conditions during road transport have been proposed and published. This has and continues to result in a significant number of target power density spectra (PDS) for use in laboratory simulation of vibrations despite the fact that the underlying parameters that influence the vibrations (road surface unevenness and vehicle dynamics) remain largely unchanged. This paper seeks to address this situation by analysing published and measured PDS (129 in total) from a variety of vehicles and road types with the aim of establishing typical PDS. Through the analysis of the frequency of the first natural mode of these PDS, it was found that there exist five typical response PDS (three for steel leaf suspension and two for air ride suspension) that can be considered as representative vibration response PDS for road transport in general. These representative PDS were matched with a two degree‐of‐freedom quarter‐car model representing the heave response of the rigid body motion of the vehicles. The analysis suggests that the higher frequency content of the measured data is not related to rigid body motion but emanates from drivetrain and, in some cases, structural vibrations. These usually comprise numerous harmonics of fixed and varying frequencies as well as transients. As these vibrations are impossible to classify based on vehicle type, it is recommended that the five quarter‐car representative spectral models proposed in this paper—namely, for steel leaf spring suspended vehicles with heavy, moderate and light loads and air ride type vehicles with heavy and light loads—be used for laboratory simulation for the purpose of evaluating the vibration resistance of products and packaged systems. It is suggested that, for most packaged product, this is sufficient to test their ability to survive road transport vibrations. These will yield more realistic vibrations than those endorsed by standards organisations and should have a positive impact on the optimisation of packaging designs and a corresponding reduction in packaging waste. Finally, the need for further work aimed at developing methods to identify and extract fixed and varying discrete frequency vibration as well as transient vibrations was identified.

A Time and Place to Land

Cooperation between an unmanned aerial vehicle (UAV) and unmanned surface vehicle (USV) can be critical in fields like search and rescue, marine ecology, and surveying. This is because, by landing a UAV on a USV for recharging, the agents can collaborate to eliminate their individual limitations; UAVs typically have a superior speed and vantage point compared to USVs, but can only remain airborne for short periods of time due to battery limitations, whereas USVs lack mobility and height but can remain at sea for extended periods of time and carry large batteries. The question our work this summer sought to answer is: how can we get a UAV-USV pair to seek out ”calm waters” and attempt a landing when and where the waves are most favorable? Due to the high risk associated with testing such a system on a real body of water, we developed a novel testbed that utilizes an unmanned ground vehicle (UGV) fitted with a custom-made 2-axis tilting landing pad that can pitch and roll to simulate the motion of a USV in a variety of wave conditions. We used this novel testbed with an indoor Crazyflie UAV to experimentally prove a cooperative model-predictive control (MPC) scheme. In essence, MPC works by optimizing control inputs using a model that encodes the dynamics of a system to pick the best series of control inputs from a finite set of valid inputs. Our work successfully proved the viability of a system where two agents use MPC to optimize their relative positions and the intensity of nearby waves to reach consensus on where and when to land. By landing 100+ times using a variety of system configurations, we were able to demonstrate a significantly increased likelihood of successful landings when using our control scheme.

Development of autonomous surface vehicles for floating garbage-collection support

Development of autonomous surface vehicles for floating garbage-collection support

Exploring Innovative Methods in Maritime Simulation: A Ship Path Planning System Utilizing Virtual Reality and Numerical Simulation

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs.

Methodology for Performing Bathymetric and Photogrammetric Measurements Using UAV and USV Vehicles in the Coastal Zone

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth.

A-Star (A*) with Map Processing for the Global Path Planning of Autonomous Underwater and Surface Vehicles Operating in Large Areas

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method’s effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path’s safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas.

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme.

Event-triggered model-free adaptive sliding-mode heading control for unmanned surface vehicles under DoS attacks

This article investigates event-triggered model-free adaptive sliding-mode heading control issues for unmanned surface vehicles with dual-channel denial of service (DoS) attacks and bounded disturbances. Initially, we establish a redefined output. Meanwhile, we establish a virtual linear data model based on the redefined output and pseudo-partial-derivative technology. In addition, to reduce the effects of the external disturbance, we introduce the sliding mode control method to enhance the system’s robustness against disturbances. Then, an event-triggered communication scheme is developed to reduce the waste of communication resources. Two compensation schemes are formulated to eliminate the effects of dual-channel DoS attacks. So, the main contributions of the presented method are the enhancement of accuracy, reduction of computational burden, and increased robustness, all of which are demonstrated in the simulation section.

Joint-optimized coverage path planning framework for USV-assisted offshore bathymetric mapping: From theory to practice

Designing effective coverage routes for unmanned surface vehicles (USVs) is crucial to improve the efficiency of offshore bathymetric surveys. However, existing coverage planning methods for practical use are limited, primarily due to the large-scale surveying areas and intricate region geometries caused by coastal features. This study aims to address these challenges by introducing a coverage path planning framework for USV-assisted bathymetric mapping, specifically aimed at the joint optimization of paths to cover numerous complex regions. Initially, we conceptualize the large-scale bathymetric survey mission as an integer programming model. The model uses four distinct decision variables to meticulously formulate length calculations, inter-regional connections, entry and exit point selections, and line sweep direction. Then, a novel hierarchical algorithm is devised to solve the problem. The method first incorporates a bisection-based convex decomposition method to achieve optimal partitioning of complex regions. Additionally, a hierarchical heuristic optimization algorithm that seamlessly integrates the optimization of all influencing factors is designed, which includes order generation, candidate pattern finding, tour finding, and final optimization. The reliability of the framework is validated through semi-physical simulations and lake trials using a real USV. Through comparative studies, our model demonstrates clear advantages in computational efficiency and optimization capability compared to state-of-the-arts, with its superiority becoming more pronounced as the problem scale increases. The results from lake trials further affirm the efficient and reliable performance of our model in practical bathymetric survey tasks.

Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

Hybrid Physics-Learning Model Based Predictive Control for Trajectory Tracking of Unmanned Surface Vehicles

Hybrid Physics-Learning Model Based Predictive Control for Trajectory Tracking of Unmanned Surface Vehicles

Terminal Line-of-Sight Angle-Constrained Target Tracking Guidance for Unmanned Surface Vehicles

Terminal Line-of-Sight Angle-Constrained Target Tracking Guidance for Unmanned Surface Vehicles

Path Following Control With Sideslip Reduction for Underactuated Unmanned Surface Vehicles

Path Following Control With Sideslip Reduction for Underactuated Unmanned Surface Vehicles

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

LDMNet: Enhancing the Segmentation Capabilities of Unmanned Surface Vehicles in Complex Waterway Scenarios

Semantic segmentation-based Complex Waterway Scene Understanding has shown great promise in the environmental perception of Unmanned Surface Vehicles. Existing methods struggle with estimating the edges of obstacles under conditions of blurred water surfaces. To address this, we propose the Lightweight Dual-branch Mamba Network (LDMNet), which includes a CNN-based Deep Dual-branch Network for extracting image features and a Mamba-based fusion module for aggregating and integrating global information. Specifically, we improve the Deep Dual-branch Network structure by incorporating multiple Atrous branches for local fusion; we design a Convolution-based Recombine Attention Module, which serves as the gate activation condition for Mamba-2 to enhance feature interaction and global information fusion from both spatial and channel dimensions. Moreover, to tackle the directional sensitivity of image serialization and the impact of the State Space Model’s forgetting strategy on non-causal data modeling, we introduce a Hilbert curve scanning mechanism to achieve multi-scale feature serialization. By stacking feature sequences, we alleviate the local bias of Mamba-2 towards image sequence data. LDMNet integrates the Deep Dual-branch Network, Recombine Attention, and Mamba-2 blocks, effectively capturing the long-range dependencies and multi-scale global context information of Complex Waterway Scene images. The experimental results on four benchmarks show that the proposed LDMNet significantly improves obstacle edge segmentation performance and outperforms existing methods across various performance metrics.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

Differential Flatness Based Unmanned Surface Vehicle Control: Planning and Conditional Disturbance-Compensation

To achieve precise control of the symmetrical unmanned surface vehicle (USV) under strong external disturbances, we propose a disturbance estimation and conditional disturbance compensation control (CDCC) scheme. First, the differential flatness method is applied to convert the underactuated model into a fully actuated one, simplifying the controller design. Then, a nonlinear disturbance observer (NDOB) is designed to estimate the lumped disturbance. Subsequently, a continuous disturbance characterization index (CDCI) is proposed, which not only indicates whether the disturbance is beneficial to the system stability but also makes the controller switch smoothly and suppresses the chattering phenomenon greatly. Indicated by the CDCI, the proposed CDCC method can not only utilize the beneficial disturbance but also compensate for the detrimental disturbance, which improves the USV’s control performance under strong external disturbances. Moreover, a trajectory-planning method is designed to generate an obstacle avoidance reference trajectory for the controller. Finally, simulations verify the feasibility of applying the proposed control method to USV.

Cold Region River Flood Mapping and Scour Potential Prediction: Insights from Hydraulic Model Using Advanced Autonomous Surface Vehicles

This study aimed to map the 2022 flood with a 16.5-year return period near a bridge on the Red River, close to Grafton City, North Dakota, and evaluate the scour potential around the bridge. The Red River Basin (RRB) near Grand Forks, ND, and Emerson, ND, is a cold region river vulnerable to floods. Local scouring around bridge piers during floods can lead to hydraulic structure failure. An Autonomous Surface Vehicle (ASV) equipped with LiDAR DEM data from the ND DWR’s LiDAR dataset was used to collect comprehensive bathymetry and discharge data, including the 2022 flood. The HEC-RAS model was used to create flood maps, and the Colorado State University (CSU) methodology was employed to assess local scour around the bridge pier. The study area recorded maximum velocities of 1.71 m/s, 1.87 m/s, and 1.56 m/s for discharge values of 368 m3/s, 784 m3/s, and 1335 m3/s, respectively, with higher velocities recorded upstream of the bridge. The maximum water depth reached 13.14 m during the peak discharge of 1335 m3/s. Higher discharge resulted in increased Froude number and contraction scour depth, with the latter continuing to increase even when the Froude number decreased as water reached the bridge deck. The study highlights the effectiveness of integrating ASVs, bathymetry, and LiDAR data to comprehensively understand flood dynamics and bridge scour in cold region rivers, offering the way for the development of effective flood control measures and strategies to safeguard critical infrastructure.