VAE-Informed Patrolling for Online Environmental Monitoring with Heterogeneous ASV-UAV Teams

In this work, we address the problem of persistent environmental monitoring using autonomous robotic teams. In prior work, we cast the task as an online patrolling problem using a fleet of Autonomous Surface Vehicles (ASVs), in which the location importance is dynamically inferred from pollutant concentration levels. However, the limited speed of ASVs can reduce performance when the phenomenon to be measured is dynamic. The availability of faster - but less precise - Unmanned Aerial Vehicles (UAVs) can reduce this issue. In this work, we investigate how using heterogeneous fleets of both ASVs and UAVs improves environmental monitoring, by combining the strengths and smoothing the limitations in terms of speed and accuracy of these two types of robots. To do so, we leverage a Variational Autoencoder based on the UNet architecture (VAE-Unet) to generate global maps of pollutant distribution from sparse and noisy measurements gathered by ASVs and UAVs. This enables informed patrolling decisions in complex, dynamic environments. Simulations inspired by a real-world use case demonstrate that our approach improves mapping accuracy and patrolling effectiveness compared to baseline and where only ASVs are used.