Multi-autonomous Underwater Vehicle Research Articles

Recovery control of autonomous underwater vehicles based on modified delay-product-type functional

This paper focuses on the recovery process of autonomous underwater vehicles, emphasizing a hierarchical framework in which autonomous underwater vehicles are categorized into a mothership and sub-vessels. In the recovery phase, following the completion of an underwater mission, sub-vessels navigate towards a location designated by the mothership. The crux of the recovery hinges on the design of the controller for adapting communication delays induced by environmental in underwater communication transmissions. The mothership and sub-vessels constitute a collaborative multi-autonomous underwater vehicles network equipped with these controllers, making them operate through the synchronized adjustment of their states represented in error terms. A delay-dependent controller condition criterion is proposed based on the modified delay-product-type Lyapunov-Krasovskii functional. The controller with the gain obtained from the criterion manages the system effectively and ensures successful recovery. The effectiveness of the proposed approach is demonstrated through a case study involving a network comprising one leading and four following autonomous underwater vehicles.

Multi Autonomous Underwater Vehicle (AUV) Distributed Collaborative Search Method Based on a Fuzzy Clustering Map and Policy Iteration

Collaborative search with multiple Autonomous Underwater Vehicles (AUVs) significantly enhances search efficiency compared to the use of a single AUV, facilitating the rapid completion of extensive search tasks. However, challenges arise in underwater environments characterized by weak communication and dynamic complexities. In large marine areas, the limited endurance of a single AUV makes it impossible to cover the entire area, necessitating a collaborative approach using multiple AUVs. Addressing the limited prior information available in uncertain marine environments, this paper proposes a map-construction method using fuzzy clustering based on regional importance. Furthermore, a collaborative search method for large marine areas has been designed using a policy-iteration-based reinforcement learning algorithm. Through continuous sensing and interaction during the marine search process, multiple AUVs constantly update the map of regional importance and iteratively optimize the collaborative search strategy to achieve higher search gains. Simulation results confirm the effective utilization of limited information in uncertain environments and demonstrate enhanced search gains in collaborative scenarios.

Distributed state constraint containment control of multi-AUV formation with unknown control direction

The control problem of multi-autonomous underwater vehicle (multi-AUV) formation systems is a hot issue in current research. The research goal is to make the formation have a more comprehensive ability to deal with emergencies under higher control accuracy. In this paper, input saturation, disturbances, and system uncertainties are considered. To achieve the convergence of system containment errors, a state constraint containment control method is proposed for multi-AUV systems with unknown control direction. Combined with unknown control direction, new auxiliary variables are constructed to compensate the input saturation. On this basis, the follower reference trajectories and errors are defined. To constrain some system states, the tan-type barrier Lyapunov function (TBLF) is proposed. The estimated values of system uncertainties and disturbances can be obtained by neural network. Compared to other works, the amount of data generated by neural network is further reduced by the optimized adaptive law in this study. The unknown control direction problem is handled based on Nussbaum function, and its limitation range is expanded. The designed control method ensures the stable operation of formation systems. The containment errors can converge to a small neighborhood of zero. By comparing the numerical simulation results with other work, the advantages of the control algorithm proposed in this paper has been verified.

Multi-Autonomous Underwater Vehicle Full-Coverage Path-Planning Algorithm Based on Intuitive Fuzzy Decision-Making

Aiming at the difficulty of realizing full-coverage path planning in a multi-AUV collaborative search, a multi-AUV full-coverage path-planning algorithm based on intuitionistic fuzzy decision-making is proposed. First, the state space model of the search environment was constructed using the raster method to provide accurate environment change data for the AUV. Second, the full-coverage path-planning algorithm for the multi-AUV collaborative search was constructed using intuition-based fuzzy decision-making, and more uncertain underwater information was modeled using the intuition-based fuzzy decision algorithm. A priority strategy was used to avoid obstacles in the search area. Finally, the simulation experiment verified the proposed algorithm. The results demonstrate that the proposed algorithm can effectively realize full-coverage path planning of the search area, and the priority strategy can effectively reduce the generation of repeated paths.

Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency.

Multi-AUV coverage path planning algorithm using side-scan sonar for maritime search

Coverage search is widely applied in the maritime military and civilian fields. To improve search efficiency and detection accuracy, we propose an effective and rapid multi-autonomous underwater vehicle (AUV) coverage path planning (ER-MCPP) algorithm using side-scan sonar (SSS). First, the given search region was optimized and modeled using grid decomposition. Next, a Voronoi diagram was used to divide and allocate the regions according to the initial position of the AUV. Subsequently, based on the idea of guidance, navigation, and control (GNC), a coverage search algorithm for a single AUV was proposed, including a path planning method combining improved lawn mower and outward-spiral methods, an integrated navigation method based on Kalman filtering, and a path tracking method considering ocean currents. In addition, we proposed a fault-tolerant control method. When an AUV fails unexpectedly, the other AUVs can accomplish a coverage search for the region through cooperation. Simulation experiments were performed using the MATLAB software. The results demonstrate that the proposed algorithm is valid. Compared with other methods, our algorithm has better robustness, finds targets more rapidly, and has a higher search efficiency. The practicality of the proposed algorithm is verified through field tests.

Dynamic task allocation in multi autonomous underwater vehicle confrontational games with multi-objective evaluation model and particle swarm optimization algorithm

In this paper, a non-zero-sum game model based on a multi-objective evaluation model was adopted to solve the dynamic task allocation problem of underwater multiple Autonomous Underwater Vehicle (multi-AUV). By introducing a functional coordination mechanism, the high degree of coordination in practical confrontation scenarios was reflected, and the autonomous decision-making and task allocation process of multiple AUVs were clarified. Secondly, a multi-objective evaluation model including survival value, strike income, and ammunition loss was constructed, and weighted processing was performed on the multiple objectives using the Analytic Hierarchy Process (AHP) method, obtaining the benefits of each non-zero-sum strategy in dynamic games. In addition, a particle swarm optimization (PSO) algorithm that combines the theory of good point sets theory (G) and speed control factors (S) for improvement, called GSPSO, was used to find the optimal strategy in the game and allocate tasks. Finally, simulation analysis showed that the collaborative system with functional coordination mechanisms significantly improved the combat capabilities of underwater multiple AUVs. The multi-objective evaluation model combined with AHP can correctly and comprehensively evaluate the advantages and disadvantages of each strategy and correctly respond to changing confrontation tasks and decision preferences. The proposed algorithm improved the convergence speed and global search ability by enhancing the diversity of the population and the effectiveness of iterative solutions, ensuring real-time decision-making and task allocation in complex high-dimensional dynamic games.

A Backbone-Network-Construction-Based Multi-AUV Collaboration Source Location Privacy Protection Algorithm in UASNs

Underwater acoustic sensor networks (UASNs) are effective instruments for monitoring marine environments and surveying seabed resources, it is important to improve their security protection, including source location privacy protection. Numerous strategies have been presented by researchers to strengthen location privacy, however, the majority of these plans have expensive energy costs. Therefore, a backbone network construction-based multi-autonomous underwater vehicle (AUV) collaboration source location privacy protection (BNCSLP) algorithm has been enhanced to address this issue. First, the nodes in the network are split into various clusters, and an entire network is segmented into various regions. The backbone network is built using clusters that house the source. To prevent the adversary’s tracking, the AUV alternately chooses alternative backbone nodes and relays the source and fake data. Then, the cluster head determines whether to update the clusters by calculating how similar the data are with nearby clusters. The nearest neighbor technique is used by the updated cluster to anticipate the data and to reduce the energy utilization of forwarding the data packets, resulting in that there is less chance of data packets being intercepted and the source location being revealed. Finally, the AUV re-plans the trajectory, which shortens the AUV’s travelling path because only fewer cluster heads need to be accessed, decreasing the time it takes for data to be transmitted.

Discrete-time AUV formation control with leader-following consensus under time-varying delays

In order to enhance the development of marine resources, this study investigates the problem of discrete-time multi-autonomous underwater vehicles (AUV) formation coordination control with leader-following consensus under time-varying delays. First, a discrete-time second-order integration model for AUVs is presented. Second, considering the existence of time-varying delays in AUV communication, the communication between multiple AUVs is classified as valid or invalid based on the maximum delay limit. The problem of AUV formation control under invalid communication is transformed into a formation control problem under switching communication topologies. Then, a time-varying delay communication synchronization strategy is designed to convert asynchronous state information into synchronous state information. Based on synchronous state information, a leader-following consensus control protocol with time-varying delays is proposed, and a controllability analysis is conducted using Lyapunov–Krasovskii functionals. Finally, the stability and effectiveness of the proposed formation-coordination controller and delay synchronization strategy were validated through simulation experiments.

Collaborative Search and Target Capture of AUV Formations in Obstacle Environments

When performing cooperative search operations underwater, multi-autonomous underwater vehicles formations may encounter array-type obstacles such as gullies and bumps. To safely traverse the obstacle domain, this paper balances convergence time, transformation distance and sensor network power consumption, and proposes a Formation Comprehensive Cost (FCC) model to achieve collision avoidance of the formations. The FCC model is used instead of the fitness function of the genetic algorithm to solve the assignment of capture positions and the improved neural self-organizing map (INSOM) algorithm is proposed to achieve efficient path-planning during the capture process. The simulation experiments in 3D space verify that the proposed scheme can improve the efficiency of robot deployment while ensuring safety.

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.

Cooperative Navigation Algorithm of Extended Kalman Filter Based on Combined Observation for AUVs

The navigation and positioning of multi-autonomous underwater vehicles (AUVs) in the complex and variable marine environment is a significant and much-needed area of attention, especially considering the fact that cooperative navigation technology is the essential method for multiple AUVs to solve positioning problems. When the extended Kalman filter (EKF) is applied for underwater cooperative localization, the outliers in the sensor observations cause unknown errors in the measurement system due to deep-sea environmental factors, which are difficult to calibrate and cause a significant reduction in the co-location accuracy of AUVs, and can even cause problems with a divergence of estimation error. In this paper, we proposed a cooperative navigation method of the EKF algorithm based on the combined observation of multiple AUVs. Firstly, the corresponding cooperative navigation model is established, and the corresponding measurement model is designed. Then, the EKF model based on combined observation is designed and constructed, and the unknown error is eliminated by introducing a previously measured value. Finally, simulation tests and lake experiments are designed to verify the effectiveness of the algorithm. The results indicate that the EKF algorithm based on combined observation can approximately eliminate errors and improve the accuracy of cooperative localization when the unknown measurement error cannot be calibrated by common EKF methods. The effect of state estimation is improved, and the accuracy of co-location can be effectively improved to avoid serious declines in—and divergence of—estimation accuracy.

Fault-Tolerant Formation Control for Multiple Stochastic AUV System under Markovian Switching Topologies

Formation control, which is a core problem in multi-autonomous underwater vehicle (AUV) systems, plays an important role in realizing safe and accurate cooperation of multi-AUV systems. This paper provides a study on fault-tolerant formation control for multiple stochastic AUV systems under Markovian switching topologies. Considering the effect of noise and Markovian switching communication topology, a novel leader-following group formation control protocol with an actuator fault for a multi-AUV system is developed under the influence of the two independent stochastic processes. The designed controller is proved by an infinitesimal generator with Lyapunov stability theory and can guarantee that the follower AUVs’ states will eventually converge to the leader’s state in each subgroup while forming the desired sub-formation. Finally, the effectiveness of the theoretical analysis is verified by a simulation experiment.

Discrete-time event-based coordinated formation control of multiple AUVs with time-varying delay and alterable topology

This article considers the issue of event-based coordinated formation control for multiple autonomous underwater vehicles subject to time-varying communication delay and alterable topology under discretized time. First, to derive the dynamics model in the discrete-time domain, the state feedback technique and the forward difference approach are employed for the continuous-time nonlinear model of autonomous underwater vehicle. For each follower, the event-triggering function and the coordinated controller are designed, using only discrete-time state information of the leader or other followers. Under this control strategy, the controller will not be updated at each discrete-time instant. Then, the coordinated control problem is turned into the asymptotic stability problem of the multi-autonomous underwater vehicle system. According to the stability analysis, sufficient conditions are presented for successfully completing the formation assignment without and with time-varying delay, respectively. Finally, by performing some numerical simulation experiments, the efficacity of our proposed event-based controller and the correctness of the main theoretical results are demonstrated. The impact of different communication conditions on the control system is revealed by the comparison of several scenarios.

Self-triggered distributed model predictive control for cooperative diving of multi-AUV system

This paper investigates the issue of cooperative diving of a multi-autonomous underwater vehicle (AUV) system using self-triggered distributed model predictive control (DMPC). Firstly, a basic DMPC approach is proposed to realize cooperative diving, where a stability condition is derived to facilitate control design. Then, the self-triggering mechanism is introduced into the basic DMPC approach to further save communication and computation resources. The triggering interval is determined with the maximum time instant satisfying the terminal constraint after solving the optimization problem, and this helps reduce the computation burden at each control loop. The feasibility and Lyapunov stability of the self-triggered DMPC approach are analyzed. Numerical simulation is provided to verify the effectiveness of the proposed method.

An adaptive event-driven fault-tolerant consensus proposed for decentralized multiple AUVs with actuator multiplicative faults

The present paper studies a distributed leaderless multi-autonomous underwater vehicle (AUV) system under a heterogeneous topology network where multiplicative faults and input saturation affect actuators, and the actuator fault severity could not be equal in each AUV. For this system, an adaptive event-driven fault-tolerant consensus control protocol is proposed. The present paper also introduces adaptive parameters in the trigger function to improve the self-regulation capability of the event-driven mechanism system. The proposed fully distributed event-driven fault-tolerant consensus strategy depends on the adaptive coupling gain of the system, and it eliminates the global information restriction. It was found that the designed event-driven controller has a good fault tolerance effect on multiplicative faults of actuators. The states of the multi-AUV system could asymptotically converge to the same conditions without Zeno behavior. Finally, the Lyapunov functional method proves the system's astringency and stability, and the numerical simulation presents the strategy's effectiveness.

A Cooperative Hunting Method for Multi-AUV Swarm in Underwater Weak Information Environment with Obstacles

Cooperative hunting is a typical task that reflects the intelligence level of a swarm. For the complex underwater weak information environment with obstacles, a problem description of the multi-autonomous underwater vehicle (AUV) cooperative hunting task is given, considering the influencing factors, including underwater obstacles, AUV sensing interaction range, and target escape strategy. A hybrid adaptive preference method based on improved artificial potential fields (HAP-IAPF) is proposed. Then the strategies of obstacle avoidance and hunting are designed separately according to the task requirements. The adaptive weight control unit is used to adjust the preference strategy. The multi-AUV cooperative hunting in dynamic obstacle underwater environments under weakly connected conditions are achieved. In order to prove the effectiveness of the proposed algorithm, simulation results compared with the traditional artificial potential field method and the optimized artificial potential field method are given in this paper. The results show that the proposed method is robust and effective in different environments.

Multi-AUV Dynamic Maneuver Countermeasure Algorithm Based on Interval Information Game and Fractional-Order DE

The instability of the underwater environment and underwater communication brings great challenges to the coordination and cooperation of the multi-Autonomous Underwater Vehicle (AUV). In this paper, a multi-AUV dynamic maneuver countermeasure algorithm is proposed based on the interval information game theory and fractional-order Differential Evolution (DE), in order to highlight the features of the underwater countermeasure. Firstly, an advantage function comprising the situation and energy efficiency advantages is proposed on account of the multi-AUV maneuver strategies. Then, the payoff matrix with interval information is established and the payment interval ranking is achieved based on relative entropy. Subsequently, the maneuver countermeasure model is presented along with the Nash equilibrium condition satisfying the interval information game. The fractional-order DE algorithm is applied for solving the established problem to determine the optimal strategy. Finally, the superiority of the proposed multi-AUV maneuver countermeasure algorithm is verified through an example.

A Novel Adaptive Filtering for Cooperative Localization Under Compass Failure and Non-Gaussian Noise

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on ‘two-master-one slave’ cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms.

The fault-tolerant consensus strategy for leaderless Multi-AUV system on heterogeneous condensation topology

The present paper proposes a distributed fault-tolerant consensus control protocol strategy for leaderless multi-autonomous underwater vehicle (AUV) systems, where actuators have a partial break-down. Instead of the global information, this strategy applies the relative state of the topology. Also, the topology graph can be any heterogeneous condensation digraph with a spanning tree in the communication network. The linear feedback technique is used to linearize the system model into a second-order multi-AUV system. Then, the Lyapunov functional method is used to prove the system's stability and astringency and the algorithm's effectiveness is simulated numerically. It was found that the actuator efficiency could not be the same in each AUV. Also, the partial actuator loss of effectiveness faults is more typical and complicated. In addition, each AUV's state can converge to the same asymptote, and the consensus error can converge to zero.