Xinjie Han

To address the high safety risks and complex navigation requirements of offshore wind farm inspection, this paper proposes a DDQN-based path-planning method for unmanned surface vehicles (USVs). The method employs a Double Deep Q-Network (DDQN) with a reward-feedback-based adaptive exploration strategy to reduce performance variation across independent training runs. Offshore wind farm geometry, dynamic-obstacle information, and the International Regulations for Preventing Collisions at Sea (COLREGs) are incorporated into the state and reward design. Experiments were conducted using three independent training seeds, with 6000 training episodes for each seed. The Adaptive DDQN achieved a mean success rate of 83.67% with a standard deviation of 3.51 percentage points, compared with 86.67% and 13.80 percentage points for the Standard DDQN. The worst-run success rate increased from 71% to 80%, indicating lower cross-run variability despite a 3.0-percentage-point reduction in the mean success rate. Additional tests with two dynamic obstacles showed that the learned policy retained task-completion capability under increased environmental complexity. These results suggest that the proposed adaptive exploration strategy can improve the cross-run consistency of DDQN training for USV navigation in dynamic offshore wind farm environments.

Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter and nonlinear object motion, which significantly degrade tracking performance. To address these challenges, this paper builds upon ByteTrack by incorporating an adaptive Kalman filtering scheme and proposing a density-aware association strategy, resulting in a novel tracker termed the Marine-Aware Adaptive Tracker (MAAT). Specifically, an adaptive Kalman filter is introduced to increase the contribution of high-confidence detections during the state update process, thereby enhancing the stability and robustness of state estimation. Furthermore, to better mitigate the frequent identity switches caused by severe platform jitter from the USV observation platform, a density-aware association strategy is proposed. This strategy dynamically adjusts the composition of the cost matrix according to the density of high-confidence targets, enabling more reliable data association under varying scene conditions. Finally, the proposed tracking algorithm is evaluated against several state-of-the-art methods on the Singapore Maritime Dataset. It achieves competitive performance, attaining 44.37 MOTA and 43.857 IDF1. Moreover, MAAT operates in real time, running at 41.4 FPS. The experimental results demonstrate that MAAT is capable of performing accurate and real-time multi-object tracking in dynamic maritime environments with surface fluctuations, thereby providing effective technical support for intelligent maritime surveillance applications.

This paper assumes that considering the unknown and time-varying nature of different strong and weak wind field disturbances and considering the nonlinear, under-driven, strongly coupled quadrotor carrying, a load is disturbed by the complex and variable wind field and unmodeled parts when flying in the real external environment, which will reduce the control effect of the nonlinear controller and make the vehicle fail to affect the flight effect. In order to ensure that the quadrotor carrying a load can carry supplies in the harsh environment for stable trajectory tracking, a neural network adaptive control algorithm is introduced in the article. The neural network algorithm has the role of online dynamic approximation, the compensation of arbitrary external disturbance and the compensation of external disturbance. Its structure is simple and low computation. In the article, the Lyapunov method is used to design the adaptive weight and estimate the weight of the online neural network, and the stability of the system is proved. Finally, the comparison of three algorithms verified by simulation proves that the above interference problem can be solved effectively by the proposed algorithm.