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Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles

Ricardo B. Grando, Victor A. Kich, Alisson H. Kolling, Junior C. D. Jesus, Rodrigo S. Guerra, Paulo L. J. Drews-Jr

TL;DR

This work presents persistent monitoring tasks for HUAUVs by combining Deep Reinforcement Learning (DRL) and Transfer Learning to enable cross-domain adaptability and lays the groundwork for scalable autonomous persistent monitoring solutions based on DRL for hybrid aerial-underwater vehicles.

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.

Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles

TL;DR

This work presents persistent monitoring tasks for HUAUVs by combining Deep Reinforcement Learning (DRL) and Transfer Learning to enable cross-domain adaptability and lays the groundwork for scalable autonomous persistent monitoring solutions based on DRL for hybrid aerial-underwater vehicles.

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.
Paper Structure (10 sections, 13 equations, 2 figures, 1 table)

This paper contains 10 sections, 13 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Setup
  5. Observations and Target Uncertainty
  6. Distributional Soft Actor–Critic (DSAC)
  7. Simulated Environments and Vehicle Description
  8. Behaviour-Based Baseline (BUG2)
  9. Experimental Results
  10. Conclusions

Figures (2)

  • Figure 1: Simulated HUAUV persistent monitoring environments. (a) Aerial domain, Env 1 (obstacle-free). (b) Aerial domain, Env 2 (four cylindrical obstacles). (c)--(d) Example LiDAR scans in Env 1 and Env 2, respectively. Underwater scenarios use the same layouts but with sonar range measurements.
  • Figure 2: Evolution of target uncertainties $\sigma_{i,t}$ during evaluation for Env 1 and Env 2 in the aerial and underwater domains. The vertical axis shows the uncertainty, and the horizontal axis is the simulation time steps. DSAC reduces the peaks and frequency of high-uncertainty intervals compared to the Bug2 baseline.