Development of an Unmanned Surface Vehicle for Harmful Algae Removal

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.

Due to the complexity and variability of the ocean environment, it is difficult for high-speed Unmanned Surface Vehicles (USV) to move safely on hydrographic surveying missions. First, the USV’s heading and speed vectors are modeled and forecasted based on the reservoir computing algorithm and the hybrid kernel function Gaussian process regression algorithm, respectively. Second, a USV safe motion strategy is developed using modeling forecast prior information and motion analysis. Finally, the ROS (Robot Operation System) -based simulation environment for USV is built to verify the effectiveness of the modeling forecasting algorithm and the safe motion strategy. The simulation of outbound, inbound and bridge crossing scenario are performed which ensures the safe motion of the USV when it performs hydrographic surveying tasks.

This article studies the design, modeling, and implementation challenges of a path-following with obstacle avoidance algorithms as guidance, navigation, and control (GNC) architecture of an unmanned surface vehicle (USV) in harbor conditions. First, an effective mathematical model is developed based on system identification, validating the USV model with field-test data. Then, a guidance system is addressed based on a line-of-sight algorithm, which uses a LiDAR as the main perception sensor for the obstacle avoidance algorithm. The GNC architecture uses a modular approach, including obstacle detection, path-following, and control in the USV platform. Finally, an implementation challenge in two control scenarios, simulation and field test, is addressed to validate the designed GNC architecture.

The problem of trajectory-tracking for Unmanned Surface Vehicle (USV) is presented using a robust adaptive approach. First, a virtual velocity command is developed using backstepping control to ensure the traj ectory tracking of the USV's location and orientation. Then, an adaptive update law is designed for the estimation and compensation of ocean currents disturbances acting on the USV system. The stability analysis and the computation of the adaptive laws are proved using Lyapunov stability. Simulation results show the validity of the proposed method for the trajectory tracking problem.

This paper describes the preliminary design, implementation, and testing of a Police Robot for Inspecting and Mapping underwater Evidence (PRIME), an Unmanned Surface Vehicle (USV) developed to aid and support police search teams in shallow-water and inshore reconnaissance operations. Manual processing of such areas can be time consuming and difficult, with dangerous debris and low visibility causing further hindrance in some scenarios. PRIME uses a combination of MultiBeam EchoSounder (MBES) and Side Scan Sonar (SSS) systems for high resolution underwater imaging. Additional navigational sensors provide PRIME with further data allowing for position control, path planning and autonomous navigation within complex environments such as inland waterways. Such applications pose significant design constraints, with the USV required to be compact and portable, relatively inexpensive and scenario-configurable. We present some approaches taken to address these challenges, based around a modular hardware and software architecture using the Robot Operating System (ROS) framework. Data gathered from field tests using initial prototypes for the detection of body-shaped test targets is also discussed.

In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles.

This paper proposed an Unmanned Surface Vehicle (USV) for water quality monitoring purposes. The USV offered high maneuverability and accurate monitoring result with IOT implementation. The paper discussed the design and development of a USV, identifying the functionality of USV sensory system, and evaluating performance of USV based on stability, velocity, and acceleration. The USV is designed based on hemisphere shape and is equipped with two brushed DC motor propellers for maneuvering purposes. The buoyancy of USV is set at 89.1% positive buoyancy for stability purposes. Dabble Gamepad controller is implemented for USV to move remotely. Temperature, pH and turbidity sensors are embedded into the USV system for monitoring purposes. Internet of Things (IoT) system is coupled with the vehicle for data monitoring via internet as it offers versatility and efficiency. The developed USV shows outstanding results in terms of maneuverability and sensors functionality. The embedded sensors reading shows stable and accurate. This developed USV will have impact in maintaining the sustainability, and wellbeing of ecosystems and health of water resources.

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.

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.

Unmanned surface vehicles (USVs) are crucial in ensuring maritime safety and observation, attracting widespread attention and research. However, a single USV exhibits limited performance and cannot effectively observe complex marine environments. In contrast, clusters of USVs can collaborate to execute complex maritime tasks, thereby enhancing the overall operational efficiency. USVs typically form heterogeneous clusters by combining vehicles with varying maneuverabilities and communication network capabilities. This has sparked an increased interest in cooperative communication research within heterogeneous USV clusters. The heterogeneous USVs discussed in this paper share the same dynamic model; however, they differ in dynamic parameters and communication capabilities. First, this study establishes a three-degree-of-freedom motion mathematical model for an underdriven USV considering environmental interference. It estimates the dynamic parameters of four USVs and evaluates their communication abilities, laying the foundation for researching the cooperative control of heterogeneous USV clusters and their application in Ocean Observation. Next, the communication capability of the USVs is assessed by studying the communication mode and signal transmission loss within the USV clusters. This study investigates the problem of cooperative communication in USV cluster formation, starting with the communication delay of USV clusters under a directed switching topology. Finally, a coherent formation controller is designed under a switching communication topology to address the dynamic transformation of communication topologies within heterogeneous USV clusters. This verifies that heterogeneous USV clusters can seamlessly form and maintain formation shapes during communication topology transformations through formation simulation experiments involving four heterogeneous USVs was 22% higher than that of dispersed control topology structures. This study provides a solid foundation for future investigations into the cooperative control of heterogeneous USV clusters and their applications in marine observations.

In this study, we developed an Unmanned Surface Vehicle (USV)-based monitoring system for simultaneous data acquisition of both the abovewater and underwater portions of a quay wall structure. To simultaneously acquire point cloud data for both the abovewater and underwater portions, we mounted LiDAR, stereo camera, and sonar on the USV platform. We developed processing and alignment techniques for the measured multi-sensor data. Field experiments were conducted on quay walls to evaluate their applicability for assessing the external surface condition of quay walls. LiDAR point cloud data, measured differently depending on the structure's material, allowed for the identification of the quay wall's surface condition by creating contrast differences in the visualization data. Stereo camera point cloud data provided the most similar representation of shape and color information. While the resolution of the underwater portion was lower than that of the abovewater portion, the sonar data allowed for the identification of underwater conditions, such as the size and shape of riprap. Because the data acquired by LiDAR, stereo camera, and sonar are point cloud data devices with embedded coordinate information, they were able to calculate the size of objects or sections of interest. Further research is needed to determine optimal operating conditions for acquiring high-resolution visualization data.

Unmanned Surface Vehicles (USVs) have widespread commercial, scientific and military applications. In some of these applications station keeping is essential. Since USVs have a bigger surface/ weight ration than large commercial ships, they are more sensitive to wind disturbances, especially during station keeping. The modeling, identification and control of a 4.9 meter long, 180 kilogram wave adaptive modular vehicle (WAM-V) USV are presented in this paper. The propulsion system of the USV is custom designed with two azimuthing thrusters, making the USV fully actuated. System identification tests were conducted to get the precise model of the USV. A sliding mode controller was first applied to the USV and a wind feedforward controller was designed to aid the nonlinear feedback control to mitigate wind disturbance. For the feedforward controller design, a wind model was formulated that uses the apparent wind speed and direction collected from a high accuracy anemometer. A model based adaptive mechanism was developed to tune the feedforward control gains for the best performance. Station keeping field tests were conducted at a location with an average wind speed of 9 knots. The results show that with the tuned feedforward controller, the USV can hold its position and heading better over extended periods of time than a single feedback controller.

Advances in robotic mapping, planning, and perception have spurred applications-based robotics research in the domain of infrastructure inspection and preservation. Though a significant portion of this research has centered around the use of unmanned aerial, ground, and underwater vehicles, research in the use of unmanned surface vehicles (USVs) is limited. USVs present a unique opportunity to capture combined maps above and below water, which is essential for the inspection of waterspanning bridges, harbors, dams, and levees. In this paper, we investigate the use of USVs for infrastructure inspection by outfitting a USV platform with a multibeam sonar, horizontally and vertically mounted lidars, several ruggedized RGB cameras, and a high-rate inertial measurement unit (IMU). By time-synchronizing all sensors, we are able to fuse information collected from lidar, camera, and IMU units via tightly-coupled lidar-visual-inertial (LVI) simultaneous mapping and localization (SLAM). We validate our methodology by collecting sensory data of an abandoned quarry and by generating a combined 3D point cloud map using lidar data, multibeam sonar data, and maximum a posteriori trajectory from the LVI SLAM approach. Experiments validate the performance of the proposed USV system, highlighting challenges in extrinsic calibration of non-overlapping sensors, sonar denoising, and refined inter-keyframe pose estimation for key-frame based SLAM approaches.