Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.

The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.

We propose an acoustic-based framework for automatically homing an Autonomous Underwater Vehicle (AUV) to the fixed docking station (F-DS) and mobile docking station (M-DS). The proposed framework contains a simultaneous localization method of AUV and docking station (DS) and a guidance method based on the position information. The Simultaneous localization and mapping (SLAM) algorithm is not available as the statistical characteristics of the measurement error of the observation system are unknown. To solve this problem, we propose a data pre-processing method. Firstly, the measurement error data of acoustic sensor are collected. Then, We propose a Variational Auto-Encoder (VAE) based Gaussian mixture model (GMM) for estimating the statistical characteristics of measurement error. Finally, we propose a support vector regression (SVR) algorithm to fit the non-linear relationship between the statistical characteristics of measurement error and its corresponding working distance. We adopt a guidance method based on line-of-sight (LOS) and path tracking method for homing an AUV to the fixed docking station (F-DS) and mobile docking station (M-DS). The lake experimental data are used to verify the performance of the localization with the estimated statistical characteristics of measurement error.

This paper presents a synergy pursuit strategy that multiple underwater robots pursue a single moving target based on finite state machine. The processes of the pursuit are defined as seven different states, and to implement each state, the corresponding strategies are designed. As the strategy for each state is implemented by a corresponding formation control, a synergy control law is also designed for multiple underwater robots. In the design of the pursuit strategy, the definition of encirclement is put forward for the underwater robots. Furthermore, the proof that a point is encircled by other points on the plane is given. To solve the problem of the concealment in the tracking and the pursuit, a semicircular pursuit strategy and a circular pursuit strategy are also proposed. Finally, based on this pursuit strategy, the simulation of whole process is carried out with the MOOS(Mission Oriented Operating Suite) platform, and the effectiveness of the semicircular and circular pursuit strategy are verified by lake experiment with practical robots. The results of simulation and lake experiment prove that this pursuit strategy can realize synergy tracking and pursuit for a single moving target under the given conditions.

Autonomous docking guidance is one of the key technologies to achieve the autonomous underwater vehicle (AUV) docking with the sub-sea docking station (DS) to realize long-term resident operation. In the process of AUV docking, the combination of long-distance acoustic guidance based on acoustic sensor and terminal visual guidance based on camera is often adopted. However, affected by the accuracy of the navigation sensor and acoustic positioning sensor carried by AUV, as well as the ocean current, AUV cannot accurately know its own position and the position of the DS, resulting in a large acoustic guidance error and the inability to enter the visual guidance stage with a reasonable deviation, thus leading to the docking failure. In this article, an improved FastSLAM algorithm is proposed to estimate the position of AUV and DS simultaneously. The positioning accuracy of traditional FastSLAM algorithm is affected by such factors as the estimation accuracy of the statistical characteristics of process noise. An improved algorithm for FastSLAM based on fuzzy Q-learning is proposed. The homing path is planned based on the Dubins theory. The path is tracked by line-of-sight guidance. The results of matlab simulation and experimental data analyzing of the portable AUV are applied to verify the effectiveness of the proposed algorithm.