Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles

TL;DR

This paper introduces a novel DRL-based framework for hybrid aerial-underwater vehicles, enabling persistent monitoring across domains by leveraging transfer learning and sensor data fusion, addressing the challenges of distinct dynamics in air and water.

Contribution

It presents a unified DRL architecture trained on lidar and sonar data, demonstrating cross-domain adaptability for hybrid vehicles in persistent monitoring tasks.

Findings

Feasibility of a shared policy for aerial and underwater environments

Effective handling of environmental uncertainty and mobile targets

Promising results in cross-domain persistent monitoring

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.