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

Lingpeng Chen, Yuchen Zheng, Apple Pui-Yi Chui, Junfeng Wu, Ziyang Hong

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.

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

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.
Paper Structure (25 sections, 14 equations, 9 figures, 1 table, 1 algorithm)

This paper contains 25 sections, 14 equations, 9 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Heterogeneous Observation Models
  4. Probabilistic Scouting Sensors
  5. Deterministic Verification Sensor
  6. Environmental Prior and Belief Dynamics
  7. Optimization Objective
  8. System Overview
  9. Global Planning: Topological Route Generation
  10. Adaptive Multi-Resolution Spatial Graph and Node Aggregation
  11. Hierarchical Workspace Decomposition
  12. Probabilistic Node Aggregation
  13. Heteroscedastic Substrate Modeling
  14. Decision Making: The Orienteering Problem
  15. Local Planning: Unified Receding Horizon Information Search
  16. ...and 10 more sections

Figures (9)

  • Figure 1: HIMoS overview. Bottom: multi-modal sensing for turbid ocean. Top: hierarchical planning framework steers the AUV to high-probability coral habitats for sampling.
  • Figure 2: System overview. An event-triggered Global Planner produces the next target region $v_{\text{next}}$ and local budget $T_{\text{local}}$. A time-triggered Local Planner receives them and optimizes a finite-horizon trajectory based on belief $\mathcal{B}$. The robot then executes the trajectory for $N_{\text{exec}}$ steps while collecting measurements and updating the belief, after which HIMoS re-plans.
  • Figure 3: Adaptive Macro region splitting process. When FLS gathers enough evidence to reduce a region's substrate uncertainty below a threshold, the corresponding Macro region is divided into Micro regions to enable high-fidelity planning.
  • Figure 4: Differentiable DLC proxy. (a) Soft visibility indicator $\alpha^{DLC}$ and gradient near the footprint boundary. (b) The resulting gradient steers the trajectory to align the footprint with target candidates.
  • Figure 5: Dataset-to-simulator pipeline.
  • ...and 4 more figures