Recently, as humans have become increasingly interested in ocean resources, underwater vehicle-manipulator systems (UVMSs) have played an increasingly important role in ocean exploitation. To realize precise operation in underwater narrow spaces, the fly arm underwater vehicle manipulator system (FAUVMS) is proposed with manipulators as its core. However, this system suffers severe dynamic coupling effects due to the combination of small vehicle and big manipulators. To resolve this issue, we propose a robust adaptive controller that contains two parts. In the first part, the extended Kalman filter (EKF) is designed to estimate the system states and predicts external disturbances to achieve adaptive control. In the second part, a chattering-free sliding mode control (SMC) is designed to converge the tracking errors to zero, thus guaranteeing the robustness of the controller. We constructed the simulation platform based on the geometric model of FAUVMS, and various simulations are carried out under different situations. Compared to the traditional methods, the proposed method has a faster convergent speed, a better robustness and adaptiveness to external disturbances, and the tracking errors of positions of the vehicle and each end-effector are much smaller.

Currently, for underwater close-range large-target localization, visual localization techniques fail since large targets completely occupy the camera’s field of view at ultraclose ranges. To address the issue, a multi-stage optical localization method combining a binocular camera and a single-point laser rangefinder is proposed in this paper. The proposed method comprises three parts. First, the imaging model of the underwater camera is modified, and a laser rangefinder is used to further correct the underwater calibration results of the binocular camera. Second, YOLOv8 is applied to recognize the targets to prepare for target localization. Third, extrinsic calibration of the binocular camera and laser rangefinder is performed, and a Kalman filter is employed to fuse the target position information measured by the binocular camera and laser rangefinder. The experimental results show that, compared with using a binocular camera alone, the proposed method can accurately and stably locate the target at close ranges with an average error of only 2.27 cm, without the risk of localization failure, and reduces binocular localization error by 90.57%.

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that estimates the changes in the center of mass (COM) and the center of buoyancy (COB) of the vehicle, and the second part is the nonlinear disturbance observer that estimates the external disturbance and model uncertainties. To attenuate the overestimation problem, a damping term is introduced to the adaptive law. The stability of the proposed method is proven on the basis of Lyapunov theory. We develop three scenarios on the Simurv platform and illustrate the effectiveness of the proposed method with a short response time and high tracking performance.