DHEA-MECD: An Embodied Intelligence-Powered DRL Algorithm for AUV Tracking in Underwater Environments with High-Dimensional Features

2026-02-08

AI Summary

Robust multi-object tracking by autonomous underwater vehicles (AUVs) remains challenging in high-dimensional, time-varying, and perturbed underwater environments. This work proposes DHEA-MECD, a deep reinforcement learning algorithm grounded in a hierarchical embodied intelligence architecture. The method innovatively integrates a dual-head encoder with an attention mechanism to decouple multi-source heterogeneous perceptual inputs and introduces a task-phase-aware multi-expert collaborative decision-making framework coupled with a Top-k expert selection strategy, enabling adaptive interference-resistant tracking across distinct motion phases. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art deep reinforcement learning methods in terms of tracking success rate, convergence speed, and trajectory optimality, thereby validating its efficacy and robustness.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In recent years, autonomous underwater vehicle (AUV) systems have demonstrated significant potential in complex marine exploration. However, effective AUV-based tracking remains challenging in realistic underwater environments characterized by high-dimensional features, including coupled kinematic states, spatial constraints, time-varying environmental disturbances, etc. To address these challenges, this paper proposes a hierarchical embodied-intelligence (EI) architecture for underwater multi-target tracking with AUVs in complex underwater environments. Built upon this architecture, we introduce the Double-Head Encoder-Attention-based Multi-Expert Collaborative Decision (DHEA-MECD), a novel Deep Reinforcement Learning (DRL) algorithm designed to support efficient and robust multi-target tracking. Specifically, in DHEA-MECD, a Double-Head Encoder-Attention-based information extraction framework is designed to semantically decompose raw sensory observations and explicitly model complex dependencies among heterogeneous features, including spatial configurations, kinematic states, structural constraints, and stochastic perturbations. On this basis, a motion-stage-aware multi-expert collaborative decision mechanism with Top-k expert selection strategy is introduced to support stage-adaptive decision-making. Furthermore, we propose the DHEA-MECD-based underwater multitarget tracking algorithm to enable AUV smart, stable, and anti-interference multi-target tracking. Extensive experimental results demonstrate that the proposed approach achieves superior tracking success rates, faster convergence, and improved motion optimality compared with mainstream DRL-based methods, particularly in complex and disturbance-rich marine environments.

Problem

• autonomous underwater vehicle
• multi-target tracking
• high-dimensional features
• underwater environments
• environmental disturbances

Innovation

• Embodied Intelligence
• Double-Head Encoder-Attention
• Multi-Expert Collaborative Decision
• Deep Reinforcement Learning
• Autonomous Underwater Vehicle