Unmanned Surface Vehicles Formation Research Articles

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.

Underactuated unmanned surface vehicles formation of obstacle avoidance and assembly: A disturbed fluid-based solution

Unmanned surface vehicles (USVs) are highly manoeuvrable and autonomous, and hold significant potential for both military and civilian applications, particularly in formation operations. However, because of their underactuated nature, USVs struggle to navigate in complex maritime conditions during formation. At present, most of the technology is devoted to Unmanned Areial Vehicles and ground robots; these methods cannot be well applied to underactuated USVs. Moreover, the rationality of local path planning decision-making for underactuated USVs formation is still lacking. This study proposes an interfered fluid dynamic system (IFDS)-based local path planning method, called USV-IFDS, specifically designed for the formation of underactuated USVs. This method incorporates the IFDS obstacle avoidance approach, while adapting it through modifications and the inclusion of the kinematic constraints of USVs, thereby enhancing its applicability to the maritime environment. By decomposing the flow field velocity vector and implementing a formation control strategy, we effectively address the challenges in forming underactuated USVs and enhance the efficiency of USV formation local path planning. The proposed formation technique is predicated on the highly robust virtual structure method. Simulations of formation local path planning indicate that our method produces smooth paths, therefore validating its practical applicability to underactuated USV formations.

Layered fully distributed formation-containment tracking control for multiple unmanned surface vehicles

This paper addresses the formation-containment tracking control (FCTC) problem for unmanned surface vehicles (USVs) formation systems swarm with multiple leaders. Unlike the general study of the multi-leader case, communication among leaders is considered to endow the system with broader applicability under different mission requirements. Besides, a layered framework is employed here to distinguish the behaviour and role of agents in the formation system. In the first layer, a formation tracking controller is developed for leaders to track the reference trajectory and achieve the desired formation configuration. Then, a containment controller is proposed for followers in the second layer to drive them into the geometric space formed by leaders. Without loss of generality, the state information transmission between the two layers is considered unidirectional and the directed communication networks are designed in both layers. Meanwhile, considering the limitation that the agents in the USV formation system can only access their local information, the FCTC control strategy is designed in a fully distributed manner. Finally, numerical simulation results are represented to demonstrate the validity and feasibility of the proposed control scheme.

Sim-real joint experimental verification for an unmanned surface vehicle formation strategy based on multi-agent deterministic policy gradient and line of sight guidance

The formation of multiple Unmanned Surface Vehicles (USVs) is an effective way to extend the capabilities of a single USV to satisfy relatively complex tasks in practice. In this study, we proposed a formation-strategy-based deep reinforcement learning method called Multi-agent Deterministic Policy Gradient (MADDPG) to realize multi-USV formation. In this work, Line of Sight (LOS) guidance is integrated into the formation strategy under a leader-follower scheme. With the advantage of ignoring the dynamic model of the USV, the proposed formation strategy has strong migration potential to be transferred to other multi-agent systems. To evaluate the performance of the multi-USV formation, we designed two different scenarios in line with the practical tasks carried out with the multi-USV system covering observation aperture enhancement with the desired formation and dynamic non-cooperative target roundup. The performance of the proposed multi-USV formation strategy was demonstrated in both a simulation environment and a real-world environment. Compared with other deep reinforcement learning-inspired and traditional approaches, our proposed strategy based on MADDPG achieved a higher task success rate. It also outperformed the Deep Deterministic Policy Gradient (DDPG) in other metrics because it can acquire knowledge more effectively from dynamic environments by observing joint information and from the centralized training.

Path Planning Method of Unmanned Surface Vehicles Formation Based on Improved A* Algorithm

Unmanned surface vehicle (USV) formation is a hot topic of current research. Path planning is the core technology for USV formation. This paper focuses on a USV formation path planning problem considering kinetic constraints. Firstly, an improved A* algorithm is proposed to solve the point-to-point path planning of a USV considering kinetic constraints. In this algorithm, the yaw constraint is introduced on top of the position constraint to extend the state space of the USV to three dimensions, and the convergence speed is accelerated by building a heuristic map. The dynamics model of the USV is used to generate the minimum trajectory elements to ensure that the path conforms to the kinetic constraints. Secondly, the mathematical model of USV formation based on the virtual structure method is established, and the path planning scheme of formation navigation and formation reconfiguration is given according to the improved A* algorithm. Finally, we carry out a USV model identification experiment for SL900 USV and simulation experiments based on the model. The experimental results show that the output path of the proposed method is smoother compared with the traditional method. This method can provide a globally safe path with kinetic constraints for USV formation navigation and formation reconstruction.

Distributed Model Predictive Consensus Control of Unmanned Surface Vehicles with Post-Verification

In this paper, the consensus control of unmanned surface vehicles (USVs) is investigated by employing a distributed model predictive control approach. A hierarchical control structure is considered during the controller design, where the upper layer determines the reference signals of USV velocities while the lower layer optimizes the control inputs of each USV. The main feature of this work is that a post-verification procedure is proposed to address the failure states caused by local errors or cyberattacks. Each USV compares the actual state and the predicted one obtained at the previous moment. This allows the estimation of local perturbations. In addition, the failure state of the USV can also be determined if a preset condition is satisfied, thus forcing a change in the communication topology and avoiding further impact. Simulations show that the proposed method is effective in USV formation control. Compared with the method without post-verification, the proposed approach is more robust when failure states occur.

Fixed-Time Formation Control for Unmanned Surface Vehicles with Parametric Uncertainties and Complex Disturbance

In this paper, under parametric uncertainties and complex disturbances, a leader–follower formation control strategy based on accurate disturbance observer (ADO) and a novel fixed-time fast terminal sliding mode (FTFTSM) control for unmanned surface vehicles (USVs) is proposed. The main contributions of this paper are: (1) A novel fixed-time fast terminal sliding mode tracking control (FTFTSM-TC) strategy is designed for the tracking control subsystem, which greatly improves the convergence rate of the leader USV in trajectory tracking. (2) An ADO is designed to observe lumped disturbances with the smallest approximation error. The ADO greatly reduces the interference of disturbances and improves the performance of the formation system. (3) An ADO-based fixed-time formation control (ADO-FTFC) strategy is developed for the formation control subsystem to maintain the desired formation. Stability of the formation control system is established by the Lyapunov theory. Simulation results show that the proposed control strategy is superior for the USVs formation control.

Back-Stepping Formation Control of Unmanned Surface Vehicles With Input Saturation Based on Adaptive Super-Twisting Algorithm

Aiming at the formation control problem for unmanned surface vehicles under input overload and external disturbance, a leader-follower formation control law based on input saturation and the adaptive super-twisting algorithm was designed in this paper. Firstly, the mathematical model of underactuated unmanned surface vehicle formation based on the leader-follower method is established, and the virtual expected velocity is designed by the back-stepping method to improve the control accuracy of formation. Secondly, the parametric uncertainties and unknown external disturbances in the USV’s dynamical model are compensated by the proposed adaptive super-twisting control laws. In addition, the input saturation function is added to the controller to avoid machine necrosis caused by input overload. Finally, Simulation results show that the controller can roughly keep the trajectory of the USV consistent with the expected trajectory and ensure that the input values are within the safe range, which proves the effectiveness of the method in this paper.

Sliding mode formation control of leaderless unmanned surface vehicles with environmental disturbances

This paper, in light of the sliding mode control (SMC) approach, concerns the formation regulation for a network of unmanned surface vehicles (USVs) with environmental disturbances. The formation pattern is configured within the consensus-based leaderless framework. Undirected connected graphs are exploited to portray the network communication in a switching manner. To begin with, thanks to the definition on relative formation error, one mathematically formulates the concerned formation control problem into the leaderless consensus design for resultant formation error systems. Then an integral-type sliding surface is constructed by a collection of neighboring USVs’ position and velocity information. Based on a characterized orthogonal transformation, the regulation of the formation performance upon the sliding surface is cast into the asymptotic stability analysis of the reduce-order sliding mode system. By partitioning the state space into agreement and disagreement subspaces, a formation reference function (FRF) with determined initial condition is derived as a disturbance-free autonomous system, whose dynamical behavior explicitly symbolizes the global movement of the leaderless USV formation system. Finally, to ensure the finite-time reachability of the sliding surface, a distributed sliding mode controller is designed by utilizing the tool from finite-time stabilization technique. As a byproduct, the theoretical analysis is extended to the case of fixed topology. The derived results are validated by some numerical examples.

Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning

Future ocean exploration will be dominated by a large-scale deployment of marine robots such as unmanned surface vehicles (USVs). Without the involvement of human operators, USVs exploit oceans, especially the complex marine environments, in an unprecedented way with an increased mission efficiency. However, current autonomy level of USVs is still limited, and the majority of vessels are being remotely controlled. To address such an issue, artificial intelligence (AI) such as reinforcement learning can effectively equip USVs with high-level intelligence and consequently achieve full autonomous operation. Also, by adopting the concept of multi-agent intelligence, future trend of USV operations is to use them as a formation fleet. Current researches in USV formation control are largely based upon classical control theories such as PID, backstepping and model predictive control methods with the impact by using advanced AI technologies unclear. This paper, therefore, paves the way in this area by proposing a distributed deep reinforcement learning algorithm for USV formations. More importantly, using the proposed algorithm USV formations can learn two critical abilities, i.e. adaptability and extendibility that enable formations to arbitrarily increase the number of USVs or change formation shapes. The effectiveness of algorithms has been verified and validated through a number of computer-based simulations.

Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

A Formation Autonomous Navigation System for Unmanned Surface Vehicles With Distributed Control Strategy

By deploying unmanned surface vehicles (USVs), the efficiency and reliability of mission execution can be improved. This paper is concerned with the problem of formation autonomous navigation system (FANS) for USVs. The decision layer of the system is composed of path planning subsystem and navigation control subsystem to realize fast and effective autonomous navigation system. The FANS is constructed by leader-follower structure and distributed control strategy, which make the individuals in the formation have certain autonomy. The dynamic domain tunable fast marching square algorithm proposed in the path planning subsystem can not only adjust the safety and length of the path planned, but also continuously re-plan the path according to the motion information of the USV formation and target-vessels. Navigation control subsystem based on finite control set model predictive control can quickly and safely guide and control formation in local sea environment. The simulation tests are carried out in static and dynamic harbor environments respectively, which verifies the ability of the FANS to achieve stable formation tracking and autonomous safe navigation.

Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking.

Multi-under-actuated unmanned surface vehicles (USV) path tracking control is studied and decoupled by virtue of decentralized control. First, an improved integral line-of-sight guidance strategy is put forward and combined with feedback control to design the path tracking controller and realize the single USV path tracking in the horizontal plane. Second, graph theory is utilized to design the decentralized velocity coordination controller for USV formation, so that multiple USVs could consistently realize the specified formation to the position and velocity of the expected path. Third, cascade system theory and Lyapunov stability are used to respectively prove the uniform semi-global exponential stability of single USV path tracking control system and the global asymptotic stability and uniform local exponential stability of coordinated formation system. At last, simulation and field experiment are conducted to analyze and verify the advancement and effectiveness of the proposed algorithms in this paper.

Fast marching square method based intelligent navigation of the unmanned surface vehicle swarm in restricted waters

Considering the limitations of a single unmanned surface vehicle (USV) in operations, such as a small mission area, lacking system autonomy and insufficient fault-tolerant resilience, there is an increasing interest in developing multiple USVs as a formation fleet in both civilian and naval areas. In order to enhance the autonomy of USVs and effectively achieve missions, an intelligent path planning algorithm and a collision avoidance control method of the USV swarm in restricted waters are presented in this paper. In terms of the path planning of the USV formation fleet, a fast marching square (FMS) method is utilized to generate an optimal path. Based on the path calculated by the FMS algorithm, collision avoidance behaviors are designed according to the International Regulations for Preventing Collisions at Sea (COLREGs), an international maritime collision avoidance regulation, to handle the collision avoidance between the USV swarm and other vessels sailing on the sea. The algorithms have been validated through computer-based simulations, which point out that the path is feasible and the USV swarm can avoid static obstacles as well as moving vessels.

Learn to Navigate: Cooperative Path Planning for Unmanned Surface Vehicles Using Deep Reinforcement Learning

Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

A Formation Collision Avoidance System for Unmanned Surface Vehicles With Leader-Follower Structure

This paper deals with the problem of formation collision avoidance for unmanned surface vehicles (USVs). Compared with the generalship formation, the formation collision avoidance system (FCAS) needs better responsiveness and stability because of faster speed and smaller volume for USVs. A method based on finite control set model predictive control is proposed to solve this problem. The novelty of the method is that it can control formation quickly to avoid obstacles and reach the destination in accordance with the dynamics of each vessel in the formation, without the prior knowledge of the environment and reference trajectory. The thruster speed and propulsion angle of the USV form a finite control set, which is more practical. The FCAS adopts the leader–follower structure and distributed control strategy to ensure that the followers have a certain autonomy. The first two simulation tests verify that the system has the formation stability, formation forming ability, and the applicability in restricted water. The last simulation test shows that the system can control the USV formation to sail quickly and safely in complex sea scenarios with formation transformation task and multiple dynamic obstacles.

Incremental predictive control-based output consensus of networked unmanned surface vehicle formation systems

This paper is concerned with the output consensus problem of networked unmanned surface vehicle (USV) formation systems under a leader-follower structure. First, a 3-degrees-of-freedom motion model is established for USV systems subject to disturbances induced by wind and waves. Second, based on the motion model, both an incremental state observer and an incremental predictive controller are constructed to investigate the effects of network-induced delays and packet dropouts on the systems under study. Third, a novel incremental predictive control scheme is developed to ensure that the outputs of all USVs in the formation reach consensus asymptotically. Finally, an illustrative example is given to verify the effectiveness of the proposed incremental predictive control scheme for networked USV formation systems.

Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment

Unmanned surface vehicles (USVs) have been deployed over the past decade. Current USV platforms are generally of small size with low payload capacity and short endurance times. To improve effectiveness there is a trend to deploy multiple USVs as a formation fleet. This paper presents a novel computer based algorithm that solves the problem of USV formation path planning. The algorithm is based upon the fast marching (FM) method and has been specifically designed for operation in dynamic environments using the novel constrained FM method. The constrained FM method is able to model the dynamic behaviour of moving ships with efficient computation time. The algorithm has been evaluated using a range of tests applied to a simulated area and has been proved to work effectively in a complex navigation environment.