AI-Driven Heterogeneous Robot Teams for Critical Infrastructure Resilience Conference

Devesa, A, Kaarlela, T, Padrao, P et al. (2025). AI-Driven Heterogeneous Robot Teams for Critical Infrastructure Resilience . OCEANS 2017 - ABERDEEN, 10.23919/OCEANS59106.2025.11244994

cited authors

  • Devesa, A; Kaarlela, T; Padrao, P; Fuentes, J; Bobadilla, L; Amini, MH

authors

abstract

  • Infrastructure inspection is a critical and resourceintensive process that is often hazardous and limited in scope when conducted manually. In this work, we present a coordinated, multi-domain heterogeneous robotic system composed of an Unmanned Aerial Vehicle (UAV), an Autonomous Surface Vehicle (ASV), and an Unmanned Underwater Vehicle (UUV) to automate infrastructure monitoring across aerial, surface, and underwater environments. Our approach combines a visibility-based coverage algorithm, a greedy set cover strategy, and Traveling Salesman Problem (TSP) path optimization to ensure efficient inspection planning. To validate the system, we conduct field experiments demonstrating autonomous launch, landing, and synchronized operation between agents. Additionally, we develop a Unity-based Digital Twin environment integrated with real-time feedback for mission planning, simulation, and training. Preliminary results from both physical and virtual deployments confirm the feasibility and effectiveness of the proposed system in enabling scalable, safe, and autonomous infrastructure inspection.

publication date

  • January 1, 2025

published in

Digital Object Identifier (DOI)