跨域持续监控用于混合空水车辆
Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles
AI总结：
本文提出利用深度强化学习和迁移学习实现混合空水车辆的跨域持续监控，展示统一策略在不同环境中的可行性。
摘要：
混合无人空中水下车辆（HUAUVs）已发展为能够在空中和水下环境中运行的平台，能够应用于具有挑战性的场景中的检测、制图、搜索和救援。然而，由于空中和水下领域的不同动态和约束，开发新方法带来了重大挑战。在本文中，我们通过结合深度强化学习（DRL）和迁移学习，提出了HUAUVs的持续监控任务，以实现跨域适应性。我们的方法采用了一个共享的DRL架构，该架构在激光雷达传感器数据（空中）和声纳数据（水下）上进行训练，展示了在两种环境中统一策略的可行性。我们进一步表明，该方法在考虑环境不确定性和多个移动目标动态的情况下表现出有希望的结果。所提出的框架为基于DRL的可扩展自主持续监控解决方案奠定了基础，适用于混合空水车辆。
英文摘要：
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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