COMBINED LIDAR AND SONAR MAPPING FOR PARTIALLY SUBMERGED INFRASTRUCTURE

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

This paper focuses on motion analysis of a coupled unmanned surface vehicle (USV)–umbilical cable (UC)–unmanned underwater vehicle (UUV) system to investigate the interaction behavior between the vehicles and the UC in the ocean environment. For this, a new dynamic modeling method for investigating a multi-body dynamics system of this coupling system is employed. Firstly, the structure and hardware composition of the proposed system are presented. The USV and UUV are modeled as rigid-body vehicles, and the flexible UC is discretized using the catenary equation. In order to solve the nonlinear coupled dynamics of the vehicles and flexible UC, the fourth-order Runge–Kutta numerical method is implemented. In modeling the flexible UC dynamics, the shooting method is applied to solve a two-point boundary value problem of the catenary equation. The interaction between the UC and the USV–UUV system is investigated through numerical simulations in the time domain. Through the computer simulation, the behavior of the coupled USV–UC–UUV system is analyzed for three situations which can occur. In particular, variation of the UC forces and moments at the tow points and the configuration of the UC in the water are investigated.

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

Hydrographic surveys must comply with the IHO S-44 standard. For the most stringent orders (Exclusive, Special, and 1a), 100% seafloor coverage is required, posing challenges in shallow waters near the 1 m isobath. Recent advancements in unmanned surface vehicles (USVs) and the miniaturisation of hydroacoustic devices now enable high-precision surveys even in hard-to-access areas. This study presents an analysis of bathymetric data coverage and density obtained using singlebeam echosounders (SBES) and multibeam echosounders (MBES) systems mounted on unmanned surface vehicle (USVs). The SBES survey employed the AutoDron USV, while MBES data were collected with the HydroDron-1 USV. Coverage analysis used a 1×1 m grid. The results reveal significant differences between the two systems. The MBES achieved an average density of 7.71 pts∙m−2 (>94% of grid cells meeting the NOAA- recommended minimum of 5 pts∙m−2). Data of MBES also exhibited uniform coverage, supporting the development of high-resolution bathymetric models. By contrast, SBES produced an average density of only 0.69 pts∙m−2, with a sparse and irregular point distribution. Only 1.79% of grid cells met the recommended threshold, while 63.79% contained no data. Nevertheless, SBES proved effective in the very shallow nearshore zone inaccessible to MBES. To achieve full coverage in compliance with International Hydrographic Organization’s requirements, complementary methods such as bathymetric light detection and ranging, Global Navigation Satellite Systems-real time kinematic surveys, or structure from motion photogrammetry are essential. Integrating these technologies is required to produce reliable and complete seafloor models.

The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology for the purpose of detecting impediments that exist on water surfaces. However, it is worth noting that these approaches lack the capability to accurately discern the precise nature or classification of those obstacles. Nevertheless, the limited optical range of unmanned vessels hinders their ability to comprehensively perceive the entirety of the surrounding information. A cooperative USV-UAV system is proposed to ensure the visual perception ability of USVs. The multi-object recognition, semantic segmentation, and obstacle ranging through USV and unmanned aerial vehicle (UAV) perspectives are selected to validate the performance of a cooperative USV-UAV system. The you only look once-X (YOLOX) model, the proportional–integral–derivative-NET (PIDNet) model, and distance measurements based on a monocular camera are utilized to realize these problems. The results indicate that by integrating the viewpoints of USVs and UAVs, a collaborative USV-UAV system, employing the aforementioned methods, can successfully detect and classify different objects surrounding the USV. Additionally, it can differentiate between navigable and non-navigable regions for unmanned vessels through visual recognition, while accurately determining the distance between the USV and obstacles.

With the rapid development of marine networks, there have been growing demands for computation-intensive and delay-sensitive marine applications and services. However, the limited underwater energy supply and the acoustic channels result in the low efficiency for computing tasks and high transmission delay. In this paper, we investigate the unmanned aerial vehicles (UAVs)-assisted multi-access computation offloading in marine networks, with the objective of minimizing the energy consumption of ocean devices. Specifically, for the underwater segment, we consider the scenario that multiple underwater sensor nodes (USNs) covered by the unmanned surface vehicle (USV) upload their sensing data via non-orthogonal multiple access (NOMA) for improving the channel utilization. For the radio frequency segment, we consider the scenario that multiple UAVs hovering in the air act as the aerial edge servers for providing computing services, in which the USV offloads the workloads to UAVs via frequency division multiple access (FDMA) for avoiding their co-channel interference, while taking into account that a malicious node overhears the USV's offloading transmission. To improve the computation offloading efficiency, we formulate an optimization problem for USNs and USV to minimize the total energy consumption by jointly optimizing the USN's uploading time, USV's computation offloading, USV's offloading time, and the secrecy provisioning. Despite the non-convexity of the formulated joint optimization problem, we exploit a layered structure to decompose the problem, and propose efficient algorithms to obtain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms. Numerical results demonstrate that our algorithms can significantly reduce the energy consumption in comparison with the benchmark schemes.

This paper describes a study on the dynamic modeling and the motion simulation of an unmanned ocean platform to overcome the limitations of existing unmanned ocean platforms for ocean exploration. The proposed unmanned ocean vehicle combines an unmanned surface vehicle and unmanned underwater vehicle with an underwater cable. This platform is connected by underwater cable, and the forces generated in each platform can influence each other’s dynamic motion. Therefore, before developing and operating an unmanned ocean platform, it is necessary to derive a dynamic equation and analyze dynamic behavior using it. In this paper, Newton’s second law and lumped-mass method are used to derive the equations of motion of unmanned surface vehicle, unmanned underwater vehicle, and underwater cable. As the underwater cable among the components of the unmanned ocean platform is expected to affect the motion of unmanned surface vehicle and unmanned underwater vehicle, the similarity of modeling is described by comparing with the cable modeling results and the experimental data. Finally, we constructed a dynamic simulator using Matlab and Simulink, and analyzed the dynamic behavior of the unmanned ocean platform through open-loop simulation.

As the use of unmanned surface and underwater vehicles (USVs and UUVs) increases, the acoustic signature and self-noise of these vehicles become important for certain type of applications. For low-detection probability the UUV or USV must have a low-acoustic signature. Furthermore, the self-noise of the UUV or USV may interfere with on-board sonar sensors, especially as more sophisticated sonar systems are being developed. As the concept of the USV or UUV is to design for low cost and low weight, with low availability of power, it is important to address the mitigation of the acoustic signature during the design phase through estimation techniques based on modeling and component level selection. Additionally during the test and integration phase, methods to measure the acoustic signature without resorting to expensive offshore measurements in “quiet” acoustic test ranges are required. In this presentation some of the approaches available for modeling and testing are discussed, together with the challenges faced by noise control engineers working these type problems and the type of noise sources that have to be addressed.

Multi-agent formation control is an important part of distributed perception and cooperation, which is convenient to complete various complex tasks and would be a key research direction in the future. This paper reviews the corresponding problems of formation control and the existing centralized and distributed formation control strategies. In particular, we discuss four types of distributed formation control methods based on position and displacement in the global coordinate system and distance and bearing in the nonglobal coordinate system, respectively. Moreover, this paper analyzes affine formation which does not require the global coordinate system. Combined with the current practical applications of multi-agent systems, the latest research for the formation control of the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV) and autonomous underwater vehicle (AUV) is given. Finally, the challenges and opportunities in this burgeoning field are discussed.

Underwater operating platforms face difficulties regarding power supply and communications. To overcome these difficulties, this study proposes a hybrid surface and underwater vehicle (HSUV) and presents the development of the platform, control algorithms, and results of field tests. The HSUV is capable of supplying reliable power to the unmanned underwater vehicle (UUV) and obtaining data in real time by using a tether cable between the UUV and the unmanned surface vehicle (USV). The HSUV uses global positioning system (GPS) and ultra-short base line sensors to determine the relative location of the UUV. Way point (WP) and dynamic positioning (DP) algorithms were developed to enable the HSUV to perform unmanned exploration. After reaching the target point using the WP algorithm, the DP algorithm enables USV to maintain position while withstanding environmental disturbances. To ensure the navigation performance at sea, performance tests of GPS, attitude/heading reference system, and side scan sonar were conducted. Based on these results, manual operation, WP, and DP tests were conducted at sea. WP and DP test results and side scan sonar images during the sea trials are presented.

With the development of the unmanned marine systems, autonomous recovery of Unmanned Underwater Vehicles(UUV) based on the Unmanned Surface Vehicles(USV) has become an important research field. Comparatively analyzing the current mainstream recovery methods, this paper proposes a new recovery scheme based on the previous research results. In this scheme, one end of the cable is connected to a winch fixed to the USV and the other is connected to the V-shaped wing, which can provide downward pressure to keep the line tight, and the rudders mounted on it can be adjusted to assure the attitude stability, which facilitate Docking and Line Capture Line Recovery(LCLR) of the UUV. After structure optimization, the V-shaped wing was modeled in hydrodynamic software STAR-CCM+. The numerical simulation shows that the improved V-shaped wing can provide about 20N downward pressure when the towing speed is 2 knots. In order to improve the reliability of the LCLR system and reduce mechanism sticking, a new type of capture mechanism is designed and it is driven by two motor respectively instead of just one. Besides, the LED beacon ranging tests were conducted in the Xiu Lake. It shows the accuracy of optical guidance algorithm is 2%, and the maximum optical recognition distance is 16m in the air and 10m in the clean water, which satisfies the docking accuracy. From October 2018, two Lake tests were carried out in Xiu Lake in Shenyang and Qiandao Lake in Hangzhou. The performance of the LCLR system was tested, and the V-shaped wing was towed at different speeds range from 1 to 1.5 knots and the length of the cable is 8m. The results show the V-shaped wing can maintain depth and attitude stability. Finally, through the improved acousto-optic joint guidance algorithm, the docking process is successfully completed and the Towed Line and Cable were captured by the UUV line capture mechanism on nose. The feasibility of the presented recovery scheme was indicated by the results from the two trials, which has a certain guiding significance for the dynamic recovery of an UUV in the future.

Efficient cleaning of floating waste using an intelligent unmanned surface vehicle is an important development trend in inland water governance. Path planning is the core of the decision-making module for unmanned surface vehicle waste cleaning and is key to achieving autonomous operation of the unmanned surface vehicle. However, due to the complexity and dynamic changes of the water surface environment, unmanned surface vehicle path planning methods for floating waste face challenges such as small size, uncertainty, and uneven distribution of floating waste. In response to the above issues, this article studies the problem of insufficient integration and low efficiency between existing path planning algorithms and target-perception modules, and designs an efficient overall path planning method for floating-waste cleaning by an unmanned surface vehicle. This method transforms the path planning problem of floating-waste cleaning unmanned surface vehicle into a Traveling Salesman Problem by setting global patrol points and tracking local targets, and proposes an improved ant colony algorithm, IACO, to solve the Traveling Salesman Problem. This article is based on the TSPLIB dataset and practical applications for experiments. The experimental results show that the proposed method has average optimal path lengths of 75.930 m, 446.555 m, and 703.759 m on the Ulysses22, eil51, and st70 datasets, respectively, which are reduced by 0.355 m, 4.108 m, and 13.575 m compared to the benchmark.

Unmanned surface vehicles have the advantages of maneuverability, concealment, wide activity area and low cost of use. Therefore, they have broad application prospects. This makes unmanned surface vehicles a research hotspot at home and abroad, and the sensing technology is the basis for the unmanned surface vehicles to perform tasks. The perception technology based on optical vision has the advantages of convenient application, relatively low cost, easy data acquisition and large amount of information, and has been widely studied by scholars at home and abroad. This paper mainly discusses the research of optical vision in unmanned surface vehicles from five aspects: Firstly, the water surface image preprocessing based on unmanned surface vehicles, mainly including water surface image defogging enhancement research; second is the use of light vision target detection; the next is the surface target tracking methods. Finally, the light vision research of unmanned surface vehicles is summarized and forecasted.

A consensus algorithm for coordinated control of unmanned surface vehicles (USVs) is studied. The USVs have different driving directions and initial velocities, so the consensus algorithm is needed to coordinate the directions and velocities of the USVs. Firstly, based on the first order linear model of USVs, get the first order nonlinear differential equations of high frequency, and the simulate environment of USVs. Then, based on the Vicsek model, the consensus algorithm in cooperative control of USVs is proposed. Aiming at the problem that USV's angular velocity lags behind the rudder angle, this paper improves the traditional PD control method, and adopts cascade PID control. So, USV can reach the same driving direction in a shorter time. Finally, simulations prove that the algorithm is feasible and compare the difference of efficiency between the PD controller and the cascade PID controller.

We consider an unmanned surface vehicle (USV) whose mission is to approach a maneuvering target ship as fast as possible followed by monitoring the target closely. The USV is equipped with sensors to detect the relative position of the target ship. Also, we assume that the USV is faster than the target and is localized using global positioning system. To achieve the mission, we develop a control law to make the USV approach the designated point (e.g., stern, left side, or right side, etc.) of the target as fast as possible. Once the USV reaches the designated point of the target, the USV checks whether the speed of the USV is fast enough to circle around the target. In the case where the circling maneuver is not feasible, the USV keeps following the designated point of the target. Once the USV determines that the circling maneuver is feasible, then we make the USV circle around the target for close monitoring, while maintaining a designated distance from the target. We present MATLAB simulations to verify our approach.