To address the challenges of manual buoy inspection, this study enhances a previously proposed Unmanned Surface Vehicle (USV) inspection system by improving its navigation and obstacle avoidance capabilities using Proximal Policy Optimization (PPO). For improved usability, the entire system adopts a fully end-to-end design, with an angular deviation weighting mechanism for stable circular navigation, a novel image-based radar encoding technique for obstacle perception and a decoupled navigation and obstacle avoidance architecture that splits the complex task into three independently trained modules. Experiments validate that both navigation modules exhibit robustness and generalization capabilities, while the obstacle avoidance module partially achieves International Regulations for Preventing Collisions at Sea (COLREGs)-compliant maneuvers. Further tests in continuous multi-buoy inspection tasks confirm the architecture’s effectiveness in integrating these modules to complete the full task.
Unmanned underwater vehicles (UUVs) face significant challenges in achieving safe and efficient autonomous navigation in complex marine environments due to uncertain perception, dynamic obstacles, and nonlinear coupled motion control. This study proposes a hierarchical autonomous…
A two-layer control framework for unmanned underwater vehicle (UUV) navigation is proposed, combining a lower-layer active disturbance rejection controller (ADRC) with an upper-layer safe reinforcement learning (RL) policy for obstacle-avoidance navigation. The lower layer, utili…
For efficient and safe navigation for an autonomous surface vehicle (ASV), this paper proposes an autonomous navigation behavior framework that integrates deep reinforcement learning (DRL) to achieve autonomous decision-making and low-level control actions in path following and c…
In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight gui…
Intelligent Guided Vehicles (IGVs) in U-shaped automated container terminals (ACTs) have longer travel paths than those in conventional vertical layout ACTs, and their interactions with double trolley quay cranes (DTQCs) and double cantilever rail cranes (DCRCs) are more frequent…
Autonomous Underwater Vehicles (AUVs) are increasingly used in deep-sea exploration, environmental monitoring, and marine engineering. Their operational safety and mission performance rely heavily on accurate and long-endurance underwater localization. However, both single-sensor…