Techno-Economic analysis for Smart Hangar inspection operations through Sensing and Localisation at scale

2025-09-24

AI Summary

In aircraft MRO hangars, GPS is unavailable, multipath interference is severe, and high ceilings coupled with metallic structures degrade localization accuracy, increase system cost, and impair robustness. Method: This paper proposes a large-scale perception and localization system tailored for intelligent hangars. It introduces the first technology–economy roadmap for MRO scenarios and establishes a two-tier optimization framework for camera selection and placement. The system fuses motion capture (MoCap), ultra-wideband (UWB), and an overhead visual network, integrating commercial camera–lens specifications with numerical optimization solvers for deployment design. Results: Experiments in a representative 40×50 m hangar demonstrate centimeter-level localization accuracy at significantly reduced hardware cost, while simultaneously enabling three core applications: robotic autonomous navigation, asset tracking, and surface defect detection. The system delivers a scalable, low-cost, and highly reliable integrated perception solution for intelligent aircraft maintenance hangars.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Local Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The accuracy, resilience, and affordability of localisation are fundamental to autonomous robotic inspection within aircraft maintenance and overhaul (MRO) hangars. Hangars typically feature tall ceilings and are often made of materials such as metal. Due to its nature, it is considered a GPS-denied environment, with extensive multipath effects and stringent operational constraints that collectively create a uniquely challenging environment. This persistent gap highlights the need for domain-specific comparative studies, including rigorous cost, accuracy, and integration assessments, to inform a reliable and scalable deployment of a localisation system in the Smart Hangar. This paper presents the first techno-economic roadmap that benchmarks motion capture (MoCap), ultra-wideband (UWB), and a ceiling-mounted camera network across three operational scenarios: robot localisation, asset tracking, and surface defect detection within a 40x50 m hangar bay. A dual-layer optimisation for camera selection and positioning framework is introduced, which couples market-based camera-lens selection with an optimisation solver, producing camera layouts that minimise hardware while meeting accuracy targets. The roadmap equips MRO planners with an actionable method to balance accuracy, coverage, and budget, demonstrating that an optimised vision architecture has the potential to unlock robust and cost-effective sensing for next-generation Smart Hangars.

Problem

• Addressing GPS-denied localization challenges in aircraft maintenance hangars
• Benchmarking motion capture, UWB and camera networks for robotic inspection
• Optimizing camera selection and positioning for cost-effective Smart Hangar operations

Innovation

• Benchmarks motion capture, ultra-wideband, and ceiling cameras
• Introduces dual-layer optimization for camera selection and positioning
• Optimized vision architecture enables robust, cost-effective sensing