Search Results (15)

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

The formation rendezvous of multiple underwater unmanned vehicles (UUVs) necessitates comprehensive consideration of various complex constraints, including kinematics, collision avoidance, and communication. However, existing methods inadequately address these constraints, making it challenging to meet practical needs. In response to these issues, this paper presents a rendezvous points allocation method and a trajectory planning method for formation rendezvous based on dynamic parameter particle swarm optimization (DPPSO) optimizing polynomial trajectories. First, various constraints involved in the formation rendezvous of UUVs are defined, including state constraints, velocity constraints, distance constraints, and turning radius constraints. Subsequently, considering the turning radius and heading angle constraints, temporary trajectories for UUVs are generated using the Dubins curve in the rendezvous points allocation. An evaluation function that accounts for trajectory length and uniformity is designed, ultimately resulting in the optimal allocation scheme. Ultimately, the polynomial trajectory planning method generates initial trajectory clusters. By integrating constraints into the DPPSO algorithm’s fitness function, the boundary conditions of the polynomial trajectories are iteratively optimized to derive trajectories that satisfy all constraints. In the simulation, the proposed method was used for rendezvous point assignment in the desired rectangular formation. The simulation results demonstrate that the proposed method provides a rendezvous point assignment solution that meets the design requirements. Furthermore, based on the rendezvous point assignment for the rectangular formation, the method proposed in this paper was applied to trajectory planning for formation rendezvous. The simulation results show that the generated trajectory successfully achieves the formation rendezvous while satisfying multiple constraints. Full article

With the increasing complexity of ocean missions, using multiple unmanned underwater vehicles to collaborate in executing tasks has become an effective way to improve the overall efficiency of ocean operations. Current research on path planning for multiple unmanned underwater vehicles mainly focuses on the basis of particle models or fully known environmental information, while research directions mainly focus on single indicators such as completion time and energy consumption. This paper first constructs a UUV model and a task scenario with detection success rate as the objective function. Then, a parameterization method based on a spiral search path was proposed for designing variables. A hierarchical control strategy is designed to ensure handle formation constraints. A general optimization framework for task scenarios has been constructed and combined with algorithms to solve optimization problems. Finally, this study compared and analyzed the performance of different optimization algorithms under the optimization framework, evaluated the optimization results of different search strategies, and explored the impact of dynamic objectives on the detection success rate. The results showed that the optimized path had a search success rate that increased by more than 50% compared to the direct path and the cover search path, which verified the effectiveness of the proposed method and strategy. Full article

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV’s flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions. Full article

This study investigates the effectiveness of UUV formations during navigation to designated target areas. The research focuses on propeller-equipped UUVs and employs a computational fluid dynamics (CFD) methodology to analyze the hydrodynamic interactions among multiple UUV formations while en route to their targeted exploration areas. Utilizing the relative drag coefficients ($r_l \mathrm{a}\mathrm{n}\mathrm{d} r_f$) and static thrust ($R_{fleets}$) as analytical parameters, this paper defines the relative distances ($a$ and $b$) between UUVs within a formation and conducts a comparative analysis of the hydrodynamic performance between individual UUVs and formation configurations. The study establishes correlations between relative distances and the hydrodynamic performance of formations. The findings reveal the following: 1. For both the lead UUV and the following UUV within the formation, the $r_l \mathrm{a}\mathrm{n}\mathrm{d} r_f$ heatmaps exhibit two distinct regions: a thrust region and a drag region. Notably, these regions significantly overlap. The maximum $r_l$ is 31.23%, while the minimum $r_f$ is −20.9%, corresponding to relative distances of $a$ = 0.12 and $b$ = 1.5. Conversely, the minimum $r_l$ is −12.2%, while the maximum $r_f$ is 22.03%, with relative distances of $a$ = 1.1 and $b$ = 0.2; 2. An analysis of formation static thrust $R_{fleets}$ reveals that it can be up to 7% greater than the drag experienced by self-propelled UUVs when relative distances $a$ and $b$ are set to 1.1 and 1, respectively. This highlights the enhanced performance achievable through formation navigation. The results presented in this paper offer valuable theoretical insights into the optimal design of relative distances within UUV formations, contributing to the advancement of UUV formation navigation strategies. Full article

The unmanned underwater vehicle (UUV) group composed of UUVs carrying different kinds of detection equipment is powerful for underwater target searching and detection. In this paper, a formation transformation method, used while the mission of the UUV group transformed from searching to detecting, is proposed. Firstly, a new formation transformation strategy, in which the UUVs converged to their detection points followed by the aggregation points achieved, is proposed to improve the safety of UUVs during transformation. Following the proposed strategy, particle swarm optimization is employed to find the optimal aggregation points. Finally, possible collisions between UUVs and collisions between UUVs and targets are avoided by adding a collision avoidance algorithm based on a virtual torque field. The model prediction is used to solve the delay problem of underwater communication, and the communication results are used to correct the model prediction results. The test shows that the method is effective. The effectiveness of the proposed method is demonstrated using two simulation examples. Full article

In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network. Full article

This paper studied the cross-domain fixed-time formation control problem of an air-sea heterogeneous unmanned system (ASHS). Taking advantage of individual characteristics of unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs), the ASHS was introduced to accomplish the ocean stereoscopic observation mission, which was transformed into the formation control problem. Due to the difference of dimension and the communication constraint between UAV, USV, and UUV, a cross-domain communication protocol was proposed to achieve the state information flow between heterogeneous unmanned vehicles with different dimensions rather than construct the identical low-dimensional output. The high-dimensional unmanned vehicle can receive full state information from low-dimensional neighbors, while the low-dimensional follower can only receive partial state information from high-dimensional neighbors. Moreover, by means of fixed-time control theory and the backstepping control technique, the distributed fixed-time observer and controller were designed to solve the formation control problem for the ASHS with disturbances. Simulation results show that the ASHS can achieve fixed-time formation control with the proposed coordinated control protocols. Meanwhile, the convergence time of the proposed fixed-time formation control protocols is independent of the initial state. Full article

In this paper, cooperative control and optimal control methods are used for the formation control of mixed-order heterogeneous multi-agent systems. The system consists of unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs). The system is represented in a state space using a block Kronecker product. The static and dynamic formation control protocols are proposed respectively, and the graph theory is used to prove that formation control protocols can realize system formation. Furthermore, the optimal control and cooperative control are introduced into the static and the dynamic formation control protocols, and the static cooperative optimal formation control protocol and the dynamic cooperative optimal formation control protocol are designed. Through MATLAB simulation, the static cooperative optimal control protocol and static formation control protocol are compared, and the dynamic cooperative optimal control protocol and dynamic formation control protocol are compared. By comparison, the state variables of the system can reach convergence quickly, and the system can complete formation in a short time, which verifies the effectiveness of the optimal theory and cooperative control. Full article

As maritime and military missions become more and more complex and multifactorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in swarm formations, towards obtaining deeper insights related to the critical issues of cybersecurity and interoperability. The research topics, which are constantly emerging in this domain, are closely related to the communication, interoperability, and secure operation of UUVs, as well as to the volume, velocity, variety, and veracity of data transmitted in low bit-rate due to the medium, i.e., the water. This paper reports on specific research topics in the domain of UUVs, emphasizing interoperability and cybersecurity in swarms of UUVs in a military/search-and-rescue setting. The goal of this work is two-fold: a) to review existing methods and tools of semantic modeling and simulation for cybersecurity and interoperability on the Internet of Underwater Things (IoUT), b) to highlight open issues and challenges, towards developing a novel simulation approach to effectively support critical and life-saving decision-making of commanders of military and search-and-rescue operations. Full article

The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before putting it into use. Considering the high cost of the UUV hardware platform and the high risk of underwater experiments, this study aims to evaluate and optimize autonomy schemes in the manner of software-in-loop (SIL) simulation efficiently. Therefore, a self-feedback development framework is proposed and a multi-interface, programmable modular simulation platform for UUV based on a robotic operating system (ROS) is designed. The platform integrates the 3D marine environment, UUV models, sensor plugins, motion control plugins in a modular manner, and reserves programming interfaces for users to test various algorithms. Subsequently, we demonstrate the simulation details with cases, such as single UUV path planning, task scheduling, and multi-UUV formation control, and construct underwater experiments to confirm the feasibility of the simulation platform. Finally, the extensibility of the simulation platform and the related performance analysis are discussed. Full article

In this paper, the formation control problem for a group of unmanned underwater vehicles (UUVs) is investigated considering collision avoidance and environment disturbances. To address the external force effect of the environment, such as waves and currents, a sliding mode disturbance observer is designed to compensate for the unknown dynamic disturbances in finite time. A bounded artificial potential field is incorporated into the control law to ensure collision avoidance among UUVs. The form of an artificial potential function is much simpler and convenient for engineering applications. A controller is devised to guarantee all the error signals are bounded, and the formation pattern can be achieved in finite time after collision avoidance. The stability of UUV formation with collision avoidance is proven by using the Lyapunov theorem, and the scheme has been shown to be convergent using Barbalat’s lemma. Comparative simulations are presented to demonstrate the effectiveness of the proposed method in 2-D and 3-D environments. Full article

This paper designs an adaptive formation control system for unmanned underwater vehicles (UUVs) in the presence of unmeasurable states and environmental disturbance. To solve the problem of unmeasurable UUV states, a filtered high-gain observer (FHGO) is employed to estimate the states, despite measurement noise. Then, an adaptive control scheme is designed to achieve UUV formation collision avoidance. The radial basis function (RBF) is used to estimate the unknown disturbance. The stability of UUV formation with collision avoidance is proven by using the Lyapunov theorem. Numerical simulation is carried out to demonstrate that the proposed filtered high-gain observer is successful in estimating the states of UUVs. The control law can keep the UUV formation from collision with good performance. Full article

This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios. Full article

Unmanned underwater vehicles (UUVs) have rapidly developed as mobile sensor networks recently in the investigation, survey, and exploration of the underwater environment. The goal of this paper is to develop a practical and efficient formation control method to improve work efficiency of multi-UUV sensor networks. Distributed leader-follower formation controllers are designed based on a state feedback and consensus algorithm. Considering that each vehicle is subject to model uncertainties and current disturbances, a second-order integral UUV model with a nonlinear function is established using the state feedback linearized method under current disturbances. For unstable communication among UUVs, communication failure and acoustic link noise interference are considered. Two-layer random switching communication topologies are proposed to solve the problem of communication failure. For acoustic link noise interference, accurate representation of valid communication information and noise stripping when designing controllers is necessary. Effective communication topology weights are designed to represent the validity of communication information interfered by noise. Utilizing state feedback and noise stripping, sufficient conditions for design formation controllers are proposed to ensure UUV formation achieves consensus under model uncertainties, current disturbances, and unstable communication. The stability of formation controllers is proven by the Lyapunov-Razumikhin theorem, and the validity is verified by simulation results. Full article