Motion-Aware Autonomous Exploration Framework for AUVs in Complex Underwater Structures

The autonomous exploration capability of Autonomous Underwater Vehicles (AUVs) in complex underwater structures is fundamentally constrained by the coupling between sensing and motion execution. Unlike ground and aerial robots, limited underwater communication makes it difficult for AUVs to rely on external computing resources for perception and motion computation, imposing higher requirements on algorithmic efficiency. Meanwhile, motion constraints require arc-shaped course adjustments rather than in-place turns, thereby increasing navigation costs. This paper focuses on fixed-depth two-dimensional exploration represented by an occupancy grid and assumes deterministic sonar observations in order to isolate the effect of sensing–motion coupling. This sensing–motion coupling makes conventional frontier-based exploration strategies inefficient for underwater environments. To address this issue, this paper proposes a motion-aware autonomous exploration framework for AUVs that jointly considers information acquisition and motion cost during exploration decision-making and path execution. The proposed framework constructs adaptive local viewpoint sampling regions directly from the real-time sonar detection coverage and introduces a weighted sampling strategy to improve viewpoint generation efficiency in structured underwater environments. A motion-aware viewpoint utility function is further designed by integrating frontier information gain, path cost, and heading deviation, enabling the exploration strategy to favor viewpoints with lower execution cost. To balance exploration efficiency and coverage completeness, a local-priority and global-backtracking target assignment mechanism is developed. In addition, motion-constrained path execution is achieved through Douglas–Peucker keypoint extraction, cubic Bezier path smoothing, and dynamic window approach (DWA)-based local trajectory tracking. Experiments on a high-fidelity Unreal Engine–ROS simulation platform show consistent reductions in exploration path length and mapping time relative to a classical frontier-based baseline, with average improvements of about 11–15% in the tested scenarios. In corridor scenarios, the adaptive viewpoint generation strategy improves local viewpoint generation efficiency by 58.5% compared with a sliding-window RRT baseline. The results demonstrate that the proposed framework can effectively improve exploration efficiency and motion consistency for AUV autonomous exploration in complex underwater structures.