Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling

2026-03-09

AI Summary

This work addresses the high energy consumption of full-coverage survey strategies and the reliance on costly vertical maneuvers in existing adaptive sampling methods for monitoring sparse benthic targets—such as coral colonies—using autonomous underwater vehicles. To overcome these limitations, the authors propose the HIMoS framework, which operates at a fixed altitude and employs a two-layer planning architecture. The global planner optimizes topological paths to maximize target discovery potential, while the local planner generates kinematically feasible trajectories through differentiable belief propagation, integrating acoustic detection, visual search, and close-range sampling. This approach innovatively enables efficient, integrated exploration and sampling of sparse targets without requiring vertical actuation. Evaluated in a high-fidelity coral reef simulation environment, HIMoS demonstrates significantly superior mission efficiency compared to state-of-the-art methods.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• sparse target search
• fixed-altitude planning
• autonomous underwater vehicles
• benthic monitoring
• coral sampling

Innovation

• hierarchical planning
• multi-modal sensing
• fixed-altitude AUV
• differentiable belief propagation
• sparse target search