An Information‐Driven Multilevel Path Planning Framework for Unmanned Surface Vehicles in Environmental Monitoring of Uninhabited Islands and Reefs

With the expansion of marine resource development, the ecological health of Uninhabited Islands and Reefs urgently requires prolonged and precise monitoring. Nevertheless, the extensive spatial scale, complex topography, and dynamic temporal variations caused by tidal processes in reef regions make it challenging for traditional methods based on predefined trajectories to balance observation efficiency and accuracy. Therefore, an Information‐Driven Multilevel Path Planning Framework is proposed to achieve dual mission requirements of topographic mapping and ecological data sampling. At the global level, the Boundary Adaptive Coverage based on Grid Completeness algorithm is proposed to enhance coarse mapping coverage in irregular island and reef areas. At the local level, an Information‐Driven Coverage Neural Network algorithm is designed to address tide‐induced variations in local terrain, enabling online fine mapping of regions of interest. At the sampling level, an Information‐Driven Travel Neural Network algorithm is proposed to address the path planning challenge involving both ocean current disturbances and energy constraints, enabling energy‐efficient planning of ecological data sampling routes. Experiments demonstrate that the proposed framework significantly outperforms existing methods in metrics such as path length, repeat coverage rate, local observation accuracy, and energy consumption control, effectively enhancing the observation efficiency and reliability of environmental monitoring tasks on Uninhabited Islands and Reefs.