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

TL;DR

This paper introduces HIMoS, a hierarchical multi-modal planning framework for fixed-altitude autonomous underwater vehicles to efficiently locate and sample sparse coral colonies, combining strategic route optimization with tactical trajectory planning.

Contribution

The paper presents a novel fixed-altitude, multi-modal planning system that integrates global and local planning for efficient coral reef monitoring and sampling.

Findings

Demonstrates superior efficiency over existing methods in simulations.

Effectively balances exploration and sampling tasks.

Validated with high-fidelity real-world survey data.

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.