固定高度下高效搜寻珊瑚，减少能耗与复杂操作
Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling
- 分层规划：全局优化路线，局部动态生成可行轨迹
- 仿真验证显示效率优于现有方法，能耗更低
- 适合深海珊瑚监测任务，尤其关注能效与精度
对珊瑚等稀疏海底现象的高效监测是自主水下航行器的重大挑战。传统全覆盖策略能耗高，而现有自适应采样方法依赖昂贵的垂直机动。为此，我们提出 HIMoS（分层信息多模态搜索）框架，实现固定高度下的稀疏珊瑚搜寻与采样任务。系统采用异构传感器套件，结合两层规划架构：战略层由全局规划器优化拓扑路径以最大化发现潜力；战术层采用可微信念传播的滚动时域局部规划器，生成兼顾声学底质探索、视觉珊瑚搜索和近距离采样的运动轨迹。在基于真实珊瑚礁底栖调查构建的高保真仿真环境中验证，该方法相较现有最优基线展现出更优的任务效率。
原文摘要 · Abstract (English)
Efficient monitoring of sparse benthic phenomena, such as coral colonies, presents a great challenge for Autonomous Underwater Vehicles. Traditional exhaustive coverage strategies are energy-inefficient, while recent adaptive sampling approaches rely on costly vertical maneuvers. To address these limitations, we propose HIMoS (Hierarchical Informative Multi-Modal Search), a fixed-altitude framework for sparse coral search-and-sample missions. The system integrates a heterogeneous sensor suite within a two-layer planning architecture. At the strategic level, a Global Planner optimizes topological routes to maximize potential discovery. At the tactical level, a receding-horizon Local Planner leverages differentiable belief propagation to generate kinematically feasible trajectories that balance acoustic substrate exploration, visual coral search, and close-range sampling. Validated in high-fidelity simulations derived from real-world coral reef benthic surveys, our approach demonstrates superior mission efficiency compared to state-of-the-art baselines.
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