用深度强化学习让空海无人机跨域持续监控，一策通吃空中水下。
Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles
- 共享强化学习架构，统一处理激光与声呐数据。
- 在多目标动态环境中实现稳定监测，适应环境不确定性。
- 适合开发可扩展的自主空海协同监控系统。
混合无人机（HUAUVs）可在空中与水下协同作业，适用于复杂场景下的巡检、测绘、搜救等任务。然而，由于空气与水体环境差异大，其自主系统开发面临巨大挑战。本文提出一种基于深度强化学习（DRL）与迁移学习的跨域持久监控方法，采用共享的DRL架构，分别以机载激光雷达数据和水下声呐数据进行训练，验证了单一策略在两类环境中的可行性。实验表明，该方法能有效应对环境不确定性及多个移动目标的动力学变化，为基于DRL的可扩展自主持久监控系统提供了基础框架。
原文摘要 · Abstract (English)
Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) have emerged as platforms capable of operating in both aerial and underwater environments, enabling applications such as inspection, mapping, search, and rescue in challenging scenarios. However, the development of novel methodologies poses significant challenges due to the distinct dynamics and constraints of the air and water domains. In this work, we present persistent monitoring tasks for HUAUVs by combining Deep Reinforcement Learning (DRL) and Transfer Learning to enable cross-domain adaptability. Our approach employs a shared DRL architecture trained on Lidar sensor data (on air) and Sonar data (underwater), demonstrating the feasibility of a unified policy for both environments. We further show that the methodology presents promising results, taking into account the uncertainty of the environment and the dynamics of multiple mobile targets. The proposed framework lays the groundwork for scalable autonomous persistent monitoring solutions based on DRL for hybrid aerial-underwater vehicles.
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