Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence
Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence
This research introduces a scene-adaptive embodied intelligence (EI) architecture for autonomous underwater vehicle (AUV) ad-hoc networks designed to execute complex maritime missions like multi-target tracking.
The paper proposes a scene-adaptive embodied intelligence architecture for multi-AUV ad-hoc networks, utilizing a dual-path critic mechanism to decouple specialized tracking tasks from global safety constraints; evaluation results claim superior tracking accuracy and accelerated policy convergence under dynamic topological fluctuations and acoustic bandwidth constraints.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2603.27194v2 Announce Type: replace Abstract: With the rapid advancement of underwater net-working 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-centricarchitectures struggle to maintain operational consistency under highly dynamic
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Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence
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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. To materialize the functional interaction between these layers, we define a beacon
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-based communication and control model that treats the communication link as a dynamic constraint-aware channel, effectively bridging the gap between high-level policy inference and decentralized physical actuation. Specifically, the proposed architecture employs a three-layer functional framework and introduces a Scene-Adaptive MARL (SA-MARL) algorithm featuring a dual-path critic mechanism. By integrating a scene c
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ritic network and a general critic network through a weight-based dynamic fusion process, SA-MARL effectively decouples specialized tracking tasks from global safety constraints, facilitating autonomous policy evolution. Evaluation results demonstrate that the proposedscheme significantly accelerates policy convergence and achieves superior tracking accuracy compared to mainstream MARL approaches, maintaining robust
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performance even under intense environmental interference and fluid topological shifts.
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Source：arXiv Robotics — research abstracts · arxiv.org