Abstract
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.
Abstract (translated)
混合无人空中水下车辆（HUAUVs）作为能够在空域和水域环境中运行的平台已经出现，使诸如检查、测绘、搜索与救援等应用在复杂场景中成为可能。然而，由于空气和水环境的独特动态特性和约束条件，开发新型方法面临着重大挑战。在这项工作中，我们通过结合深度强化学习（DRL）和迁移学习来提出一种适用于HUAUVs的持续监测任务的方法，以实现跨域适应性。我们的方法采用了一种共享的DRL架构，并利用激光雷达传感器数据（空中）和声纳数据（水下）进行训练，展示了针对这两种环境制定统一策略的可能性。 我们进一步表明，所提出的方法在考虑到环境不确定性以及多个移动目标的动力学情况下，能够取得令人鼓舞的结果。该框架为基于DRL的混合空海无人车辆的大规模自主持续监控解决方案奠定了基础。
URL
https://arxiv.org/abs/2602.21259
