Multiple Unmanned Surface Vehicles Research Articles

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.

Finite-time fuzzy adaptive consensus control for state-constrained MIMO nonlinear MASs with switched topologies

In this article, the adaptive fuzzy finite-time output feedback consensus control problem is investigated for multiple input and multiple output (MIMO) nonlinear multi-agent systems (MASs) with switched topologies and asymmetric state constraints. Fuzzy logic systems (FLSs) are utilised to approximate uncertain nonlinear dynamics, and a distributed observer and a state observer are established to estimate the unknown leader and system states, respectively. By constructing the barrier Lyapunov functions (BLFs), a finite-time output feedback consensus control scheme is presented via backstepping control technique. It is proved that the controlled MASs are stable, and the consensus errors converge in finite time. Moreover, the system states will not exceed the given bounds. Finally, the proposed finite-time consensus control approach is applied to control multiple unmanned surface vehicles (USVs), the simulation results check the feasibility of the developed finite-time consensus control methodology.

Self‐organizing cooperative hunting for unmanned surface vehicles with constrained kinematics

SummaryThe article aims at solving a cooperative hunting problem for multiple unmanned surface vehicles (USVs) subject to constrained kinematics. In order to cooperatively trap the evader into the hunting domain, a velocity model with control variable for the pursuers is firstly proposed according to the Apollonius circle. Then, a flexible self‐organizing control strategy is developed, which enables the pursuers to approach the evader while forming an encirclement. The pursuers can dynamically adapt their strategies in real‐time by choosing the optimal control variable. Additionally, take into account the limitation imposed on the vessel's motion, the optimal control variable with constraint can be obtained by using the particle swarm optimization with log‐barrier method. The simulation results ultimately demonstrate the validity and superiority of the proposed cooperative hunting algorithm.

Distributed Optimization-Based Path Planning for Multiple Unmanned Surface Vehicles to Pass through Narrow Waters

Safety and efficiency are important when Unmanned Surface Vehicles (USVs) pass through narrow waters in complex marine environments. This paper considers the issue of path planning for USVs passing through narrow waterways. We propose a distributed optimization algorithm based on a polymorphic network architecture, which maintains connectivity and avoids collisions between USVs while planning optimal paths. Firstly, the initial path through the narrow waterway is planned for each USV using the narrow water standard route method, and then the interpolating spline method is used to determine its corresponding functional form and rewrite the function as a local cost function for the USV. Secondly, a polymorphic network architecture and a distributed optimization algorithm were designed for multi-USVs to maintain connectivity and avoid collisions between USVs, and to optimize the initial paths of the multi-USV system. The effectiveness of the algorithm is demonstrated by Lyapunov stability analysis. Finally, Lingshui Harbor of Dalian Maritime University and a curved narrow waterway were selected for the simulation experiments, and the results demonstrate that the paths planned by multiple USVs were optimal and collision-free, with velocities achieving consistency within a finite time.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Multi-USV cooperative oil spill source seeking via extremum seeking combined with information fusion

This paper addresses the issue of cooperative source seeking for oil spill pollution by multiple unmanned surface vehicles (USVs), where the oil spill plume propagation is modeled by a linear advection–diffusion equation in two-dimensional space. The main objective is to construct a cooperative source seeking scheme for a group of USVs in the framework of extremum seeking combined with information fusion to solve such cooperative source seeking problem. To do this, both a gradient-based control law and a formation control law are included in the cooperative source seeking scheme. The gradient-based control law that is constructed by extremum-seeking control combined with information fusion drives the multiple USVs to approach the oil spill source, where the gradient is estimated online via the extremum-seeking control and the fused concentration value that is derived via a linear H∞ filter from the local concentration measurements at the USV’s current position. In the source seeking process, the USVs driven by the formation control law based on the virtual structure method maintain a preset circular formation. The convergence performance of the cooperative source seeking scheme is analyzed rigorously by the Lyapunov method. Finally, extensive numerical simulations are included to validate the theoretical algorithm.

Distributed global output-feedback formation control without velocity measurement for multiple unmanned surface vehicles

This article studies the distributed formation control problem for multiple unmanned surface vehicles (USVs) considering uncertain coefficient matrixes, unmeasurable velocities, and time-varying disturbances. The main contributions are as follows: First, a global coordinate translation is proposed to partially linearize the nonlinear dynamic model equipped with the unmeasurable velocity. Second, based on the global coordinate translation, a novel type of fixed-time extended two-state observer (FTETSO) is developed to estimate unmeasurable velocities and total disturbances for each vehicle. Wherein, the estimation errors will converge to zero within a fixed time. Meanwhile, considering estimation accuracy, a two-state extension is proposed to replace a single-state extension. Third, using a sliding model-based control technique, an FTETSO-based distributed global output-feedback fixed-time formation controller (GOFFC) is elaborately developed. Based on the proposed controller, the fixed-time convergence of the closed-loop system is ensured. Finally, the validity and stability of the proposed control approach are verified by simulations.

Distributed fault-tolerant pinning control for cluster synchronization of multiple unmanned surface vehicles

Under directed communication topology, this article investigates the distributed fault-tolerant cluster synchronization problem for multiple unmanned surface vehicles. Nonlinear uncertainties, external disturbances, and actuator failures are considered. The negative effect caused by thruster faults can be attenuated by utilizing the adaptive input compensation strategy. The neural network scheme is utilized to estimate nonlinear uncertainties and external disturbances. By choosing some unmanned surface vehicles as pinning nodes, a distributed fault-tolerant pinning control algorithm is presented. The cluster synchronization for multiple unmanned surface vehicles can be guaranteed under the proposed control method. The Lyapunov technique is utilized to demonstrate the stability of the multiple USV system. The feasibility of the novel strategy is demonstrated by numerical simulation.

Global finite-time guidance algorithm and constrained formation control using novel nonlinear mapping function for underactuated multiple unmanned surface vehicles

The formation control issue of multiple underactuated unmanned surface vehicles (USVs) with state constraints is discussed in this work. First, the leader–followers system is used to present a global finite-time guidance algorithm for followers with velocity constraints. This algorithm can handle the case where the starting formation is not the intended formation. Second, the state constraints of the USV control subsystem is then handled by a novel nonlinear mapping function (NMF), which has the following benefits: (1) The designed NMF can respond to changing conditions with flexibility since it can handle symmetric constraints. (2) The situation where constraint bounds trend to infinity can be handled by it. (3) The designed NMF has a speed factor that enables it to be modified to track speed. Third, finite-time constrained controllers are proposed to provide formation control for multiple USVs by utilizing the unconstrained system. Next, while guaranteeing that the states are contained within the bounds, the theoretical analysis verifies that the system tracking error stabilizes in finite time and establishes the boundedness of all tracking error signals. To confirm the accuracy and superiority of the theoretical results, two case studies are created.

Multi-vessel target tracking with camera fusion for unmanned surface vehicles

With the decreasing availability of sailors, there has been an increasing focus on the development of autonomous ships. Among the various components of autonomous ships, automatic recognition systems that can replace human vision are a crucial area of research. While ongoing studies utilize traditional perception sensors such as RADAR (RAdio Detection And Ranging) and AIS (Automatic Identification System), they have limitations such as blind spots and a restricted detection range. To address these limitations, this paper proposes a new recognition method that utilizes multiple cameras, including electro-optical and infrared radiation cameras, to supplement traditional perception sensors. This method aims to detect maritime obstacles accurately and estimate their dynamic motion using a tracking process. Initially, real-sea images were collected for maritime obstacle detection, and a deep-learning-based detection model was trained on them. The detection results were then employed in an adaptive tracking filter, which allowed the precise motion estimation of the obstacles. Furthermore, to compensate for the limitations of using individual cameras as sensors, this study introduces the simultaneous fusion of tracked data from multiple cameras. This fusion process enhances tracking results in various ways. In field tests using multiple Unmanned Surface Vehicles (USVs), the proposed method successfully converged tracking results within the range of GPS errors. In addition, the fusion of tracked data from multiple cameras significantly improved the tracking results obtained from a single camera.

Distributed Optimal Formation Control of Multiple Unmanned Surface Vehicles with Stackelberg Differential Graphical Game

Distributed Optimal Formation Control of Multiple Unmanned Surface Vehicles with Stackelberg Differential Graphical Game

Cooperative Target Enclosing Control for Multiple Unmanned Surface Vehicles with Unknown Dynamics and Safety Assurance

Cooperative Target Enclosing Control for Multiple Unmanned Surface Vehicles with Unknown Dynamics and Safety Assurance

Scalable-MADDPG-Based Cooperative Target Invasion for a Multi-USV System.

This article concentrates on proposing a scalable deep reinforcement learning (DRL) method for a multiple unmanned surface vehicle (multi-USV) system to operate cooperative target invasion. The multi-USV system, which is made up of multiple invaders, needs to invade target areas in a specified time. A novel scalable reinforcement learning (RL) method called Scalable-MADDPG is proposed for the first time. In this method, the scale of the multi-USV system can be changed at any time without interrupting the training process. Then, to mitigate the policy oscillation after applying Scalable-MADDPG, a bi-directional long-short-term memory (Bi-LSTM) network is constructed. Moreover, an improved ϵ -greedy strategy is proposed to help balance the exploration and exploitation in RL. Furthermore, to enhance the robustness of the optimal policy, Ornstein-Uhlenbeck (OU) noise is added in this improved ϵ -greedy strategy during the training process. Finally, the scalable RL method is used to help the multi-USV system perform cooperative target invasion under complex marine environments. The effectiveness of Scalable-MADDPG is demonstrated through three experiments.

Adaptive fuzzy finite-time event-triggered formation control for unmanned surface vehicle systems

This paper studies the finite-time event-triggered fuzzy formation control problem for multiple unmanned surface vehicle (USV) system. The fuzzy state estimator is designed by intermittent output signal to estimate unmeasurable velocity and yaw velocity of the USV. The dual-channel event-triggered mechanisms (ETMs) are designed to reduce the updating times from sensor-to-actuator and controller-to-actuator channels, respectively. Based on the designed fuzzy state estimator and ETMs, a finite-time fuzzy adaptive output feedback formation control is formulated. It is proved that the controlled USV system is semi-global practically finite-time stable. Moreover, the outputs of the followers are able to track the output of the leader. Finally, the simulation and comparison results are provided to demonstrate the feasibility of the presented formation control algorithm.

A Formation Control and Obstacle Avoidance Method for Multiple Unmanned Surface Vehicles

This study introduces a method for formation control and obstacle avoidance for multiple unmanned surface vehicles (USVs) by combining an artificial potential field with the virtual structure method. The approach involves a leader–follower formation structure, where the leader autonomously avoids collisions using an artificial potential field based on the target’s position as a reference. It also determines the ideal position of each follower in the formation based on its own position, heading angle, and the formation structure. To effectively avoid obstacles and maintain formation, the follower selects the position of the target or its ideal position as a reference during movement, depending on whether it is being repelled by obstacles. Additionally, this paper modifies the attractive force model of the traditional artificial potential field method to restrict the maximum magnitude of the attractive force when encountering repulsive forces, thus expediting departure from obstacle areas. The dynamic characteristics of USVs are taken into account by constraining the maximum linear speed and angular speed. Formation stability is ensured by maintaining a constant speed for the leader, while the linear speed of the follower varies based on the distance to the reference object during movement. Simulation experiments demonstrated that this method can effectively avoid obstacles and maintain formation.

Adaptive prescribed-time containment control for multiple unmanned surface vehicles with uncertain dynamics and actuator dead-zones

This paper investigates the containment control problem of multiple unmanned surface vehicles (USVs) with directed communication topology. The unknown dynamics, external disturbances and actuator dead-zones of each USV are taken into account. Firstly, an event-triggered quantized (ETQ) control strategy and an improved prescribed-time control technique are combined, such that a balance is achieved between reducing the channel burden and ensuring control performance. Then, with the aid of backstepping method, finite-time differentiators, neural networks and parametric adaptive techniques, a fully-distributed adaptive containment controller is developed. The idea of single-parameter learning and estimating the upper bound of disturbances greatly reduce the calculation of the controller. Importantly, the degradation of the control performance caused by saving channel resources and reducing calculation is not worth worrying about, thanks to the performance prescribed control scheme. It is proved that all signals in the closed-loop system are bounded and the containment error of the multi-USV system is semi-globally practically finite-time stable. Finally, the effectiveness of the proposed controller was verified by simulations of 1:70 scale ship models.

Dynamic event-triggered adaptive NN consensus control for nonlinear multiagent systems with unknown measurement sensitivity

In this paper, the event-triggered adaptive neural network (NN) asymptotic consensus control problem is studied for nonlinear multi-agent systems (MASs) with unknown measurement sensitivity. A new distributed filter is designed to estimate the leader and solve the nondifferential problem of virtual controllers caused by the noncontinuous consensus error. The unknown nonlinear dynamics and measurement sensitivity are handled by using the NN approximators and adaptive estimation method, respectively. To avoid unnecessary data transmissions, a dynamic event-triggered mechanism (DETM) composed of the measurement and control channels is designed. Then based on backstepping technique, an asymptotic consensus controller is obtained. It is proved that the closed-loop signals are all bounded, and the consensus error asymptotically converges to the origin. Finally, to show the effectiveness of the developed control method, we apply the developed control method to multiple unmanned surface vehicles (USVs).

Path Planning for Multiple Unmanned Surface Vehicles Using Glasius Bio-Inspired Neural Network With Hungarian Algorithm

In this article, we focus on the cooperative path planning for unmanned surface vehicles (USVs) composed of single-USV path planning and multi-USVs task assignment. First, the Glasius bioinspired neural network (GBNN) is used to calculate the neural activity for the discretized working space of USV, and the ocean current is considered in the definition of neuron connection weight especially. The standard path of single USV for obstacle avoidance between start point and destination can hence be planned. Then, based on the result of neural activity values from GBNN, the cost matrix of the Hungarian algorithm is built and modified for the task assignment among multi-USVs, and the unbalanced problem is well resolved. Consequently, the task points are allocated to USVs and prioritized effectively, and each USV just needs to visit the corresponding task points respectively along the standard paths by GBNN to accomplish the task. Finally, the simulation results demonstrate the feasibility and efficiency of the proposed algorithm.

Deep hierarchical reinforcement learning based formation planning for multiple unmanned surface vehicles with experimental results

In this paper, a novel multi-USV formation path planning algorithm is proposed based on deep reinforcement learning. First, a goal-based hierarchical reinforcement learning algorithm is designed to improve training speed and resolve planning conflicts within the formation. Second, an improved artificial potential field algorithm is designed in the training process to obtain the optimal path planning and obstacle avoidance learning scheme for multi-USVs in the determined perceptual environment. Finally, a formation geometry model is established to describe the physical relationships among USVs, and a composite reward function is proposed to guide the training. Numerous simulation tests are conducted, and the effectiveness of the proposed algorithm are further validated through the NEU-MSV01 experimental platform with a combination of parameterized Line of Sight (LOS) guidance.

Event-triggered fuzzy adaptive output feedback containment fault-tolerant control for nonlinear multi-agent systems against actuator faults

In this paper, the observer-based fully distributed event-triggered adaptive fuzzy containment fault-tolerant control (FTC) problem is studied for nonlinear multi-agent systems (MASs) subject to unknown actuator faults. Fuzzy logic systems (FLSs) are utilized to model unknown agents, and a new state observer is constructed to estimate unmeasurable states. Since the global communication information of topology is not available, the local reference signals are also not available, a distributed estimator is introduced to obtain the information of the local reference signals. To deal with the unknown actuator faults, a novel actuator fault compensation mechanism with a projection operator is adopted. Then based on backstepping technique and by designing a two-channel event-triggered mechanism, a fully distributed event-triggered containment FTC approach is developed. It is testified that all parameters and variables of the controlled system are bounded, and the followers can converge to the dynamic convex hull spanned by the leaders. Finally, we apply the developed control algorithm to the multiple unmanned surface vehicle (USV) systems, and the corresponding simulation results further confirm the feasibility of the developed control algorithm.