Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence
With the rapid advancement of underwater networking and multi-agent coordination technologies, autonomous underwater vehicle (AUV) ad-hoc networks have emerged as a pivotal framework for executing complex maritime missions, such as multi-target tracking. However, traditional data-centric architectures struggle to maintain operational consistency under highly dynamic topological fluctuations and severely constrained acoustic communication bandwidth.
This article proposes a scene-adaptive embodied intelligence (EI) architecture for multi-AUV ad-hoc networks, which re-envisions AUVs as embodied entities by integrating perception, decision-making, and physical execution into a unified cognitive loop. This innovative approach aims to enhance the efficiency and effectiveness of AUV operations in challenging underwater environments.
Key Features of the Proposed Architecture
- Beacon-Based Communication Model:
The architecture introduces a beacon-based communication and control model that treats the communication link as a dynamic constraint-aware channel. This effectively bridges the gap between high-level policy inference and decentralized physical actuation.
- Three-Layer Functional Framework:
The proposed architecture employs a three-layer functional framework designed to facilitate the interaction between perception, decision-making, and action execution layers.
- Scene-Adaptive MARL Algorithm:
The article introduces a Scene-Adaptive MARL (SA-MARL) algorithm featuring a dual-path critic mechanism. This mechanism integrates a scene critic network and a general critic network through a weight-based dynamic fusion process.
- Decoupling Specialized Tracking Tasks:
SA-MARL effectively decouples specialized tracking tasks from global safety constraints, facilitating autonomous policy evolution and ensuring that the AUVs can adapt to various environmental conditions.
Performance Evaluation
Evaluation results demonstrate that the proposed scheme significantly accelerates policy convergence and achieves superior tracking accuracy compared to mainstream MARL approaches. The architecture maintains robust performance even under intense environmental interference and fluid topological shifts.
This advancement in embodied intelligence for multi-AUV networks not only improves tracking capabilities but also enhances the overall operational reliability of AUV missions. By addressing the key challenges posed by dynamic underwater environments, this research paves the way for more effective maritime operations, potentially transforming how multi-target tracking is conducted in complex aquatic settings.
Conclusion
In summary, the integration of scene-adaptive embodied intelligence into multi-AUV ad-hoc networks represents a significant leap forward in the field of underwater robotics. As these technologies continue to evolve, they hold the promise of enabling more sophisticated and resilient autonomous systems capable of tackling an array of maritime challenges.