The localization of autonomous underwater vehicles (AUVs) in anchor-free environments has always been a difficult problem due to the lack of global positioning systems and absolute references. In general, AUVs localize themselves by dead reckoning (DR), whereas the localization error grows without bound. To alleviate the growth of the localization errors, we propose intermittent belief propagation based dead reckoning (IBPDR) as a cooperative localization (CL) framework. In IBPDR, AUVs use DR to localize themselves and periodically correct DR’s deviation with CL methods. The intermittent feature of IBPDR reduces communication costs among AUVs by decreasing the frequency of CL. In the IBPDR framework, we design a particle-based underwater-adaptive belief propagation (UABP) algorithm for CL. The UABP algorithm is naturally distributed and viable in nonlinear and non-Gaussian situations. Thus, it is suitable for CL issues. Furthermore, the UABP algorithm is robust to the accumulated inertial measurement errors and reduces communication costs among AUVs. Moreover, we propose a particle-based current-aided filter to further improve the localization accuracy by comparing AUVs’ ambient current observations with the available current maps. Simulation results validate the proposed algorithms by comparisons with alternative approaches in localization accuracy, communication costs, and robustness to abnormal cases, such as packet loss, ranging bias, and outliers.

Target approaching is an essential ability in marine applications, such as underwater rescue. The recent advances in autonomous underwater vehicles (AUVs) make them ideal options to deliver the target approaching in anchor-free environments, where no device with a known position (anchor) is pre-deployed. To approach a moving target, the trajectory planning of AUVs is an indispensable technical guarantee. However, due to the lack of anchors and the error accumulation during the navigation, the position errors of AUVs grow without bound and degrade trajectory planning performance. Thus, an AUV localization procedure is required and should be used in combination with the trajectory planning. In this paper, we research the multiple-AUV trajectory planning based on AUV cooperative localization and propose a framework named multiple-AUV cooperative localization based trajectory planning (MCLTP). Under MCLTP, scalable cooperative localization for multiple AUVs (SCLMA) and trajectory planning for multiple AUVs (TPMA) algorithms are derived. The design of SCLMA focuses on improving the localization accuracy by alleviating the accuracy degradation caused by the accumulated errors in the inertial measurements. TPMA formulates the trajectory planning into a convex optimization problem with balanced target detection probability and tracking accuracy. Furthermore, the considerations of the practical constraints, such as the kinematic and communication limitations of AUVs, collision and obstacle avoidance, make TPMA reliable in the complex marine environments. Simulation results reveal the advantages of the proposed methods by comparisons with the state-of-the-art alternative algorithms.

This article presents a hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) cooperative localization algorithm, termed HTACL, for the multiple-autonomous-underwater-vehicle (multi-AUV) navigation in the absence of anchors (devices with known positions). Compared with traditional TOA-based methods, the joint use of TOA and AOA measurements improves the quality of position prediction and inter-AUV relative position information in localization. Specifically, by exploring the relationship between AOA measurements and AUV attitudes, the impact of the accumulated errors in inertial measurements on the position prediction is alleviated. Moreover, the directivity and resolution of relative position representation is improved through involving AOA information. As a result, the localization accuracy can be improved by HTACL. The design of HTACL also considers the influence of the long propagation delay during AUV cooperation to enhance its adaptability to harsh underwater environments. Moreover, HTACL is naturally distributed and with good scalability. The abovementioned features make it applicable and suitable for multi-AUV localization issues. Through various simulations, the advantages of HTACL are verified and it could stably provide better localization accuracy compared with the state-of-the-art alternative methods under different conditions.

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV's actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.

In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time‐varying external disturbances of currents and waves in the sea. To solve the problem, a fixed‐time extended state observer (FTESO)‐based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed‐time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed‐time convergence but also reduces the trajectory tracking errors of the AUV.