ARIES: Autonomous Reconnaissance, Inspection and Exploration System for Hybrid Single and Multi-UAV Building Inspection Approach after Disaster

Abstract

We present ARIES a multi-agent single and multi-Unmanned Aerial Vehicles (UAV) approach for full building inspection following disasters in search and rescue operations (SAR). Our system addresses compute challenges and time constraints of autonomous inspections. ARIES employs a two-pronged strategy. We deploy a robust single UAV for exterior inspection, identifying potential failure points from structural damage (cracks and spalls). Identified points are priority areas for intelligent interior swarm exploration. These interior UAVs use thermal imaging to identify people in low-light. ARIES combines deep neural networks for vision tasks and heuristic algorithms for path planning tasks. It leverages You Only Look Once model (YOLO) variants YOLOv8-nano and YOLO12-nano for inference on edge devices and efficient lightweight path planning using Travelling Salesman Problem, Rapidly-Exploring Random Tree∗, A∗, and D∗ Lite. This work compares trade-offs for efficient computational offloading in a real-world application. We show that onboard computation on a Raspberry Pi can detect exterior structural damage in ∼2s at 0.64 and 0.67 recall for cracks and spalls respectively. Additionally, at Ground Control Station, we achieve 0.85 recall for human detection during interior inspection.