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The work addresses the lack of comprehensive real-world techno-economic comparisons by benchmarking motion capture (MoCap), ultra-wideband (UWB), and ceiling-mounted camera networks across three operational scenarios. It proposes a dual-layer camera selection and positioning optimisation that reduces hardware while meeting accuracy targets, supporting cost-, coverage-, and integration-balanced deployments for smart hangars.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/techno-economic-analysis-for-smart-hangar-inspection-operations-through-sensing-and-localisation-at-scale/664679/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/techno-economic-analysis-for-smart-hangar-inspection-operations-through-sensing-and-localisation-at-scale/664679.png","ImageObject",300,407,{"name":92,"@type":93},"sagouaw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is localisation considered challenging in MRO hangars?","Question",{"text":112,"@type":113},"MRO hangars create a GPS-denied environment with tall ceilings, metallic structures, extensive multipath effects, and stringent operational constraints that together make localisation uniquely difficult.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which sensing approaches does the paper benchmark, and for what scenarios?",{"text":117,"@type":113},"It benchmarks MoCap, UWB, and a ceiling-mounted camera network for robot localisation, asset tracking, and surface defect detection within a 40 × 50 m hangar bay.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the dual-layer optimisation framework introduced in the study?",{"text":121,"@type":113},"The framework couples market-based camera-lens selection with an optimisation solver and produces camera layouts that minimise hardware while still meeting accuracy targets.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},664679,1793266773,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},3985747862151,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","arXiv :2509 .20229v1 [ cs .RO] 24 Sep 2025  \nTechno-Economic analysis for Smart Hangar inspection operations through Sensing and Localisation at scale  \nAngelos Plastropoulos1,* , Nicolas P. Avdelidis2 , and Argyrios Zolotas1  \nAbstract  \nThe 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 ceilingmounted camera network across three operational scenarios: robot localisation, asset tracking, and surface defect detection within a 40 × 50 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.  \nKeywords: aircraft maintenance, motion capture systems, ultra-wideband systems, localisation, smart hangar, monitoring, defect detection, MRO, robotic inspection, hangar of the future, smart infrastructure.  \n1 Introduction  \nAccurate localisation is fundamental to enabling autonomous robotic inspection in MRO hangars, where metallic structures, extensive multipath effects, and strict operational constraints define a unique and challenging environment [1] . Existing localisation technologies, including infrared MoCap, UWB realtime location systems, and camera-based or Simultaneous Localisation and Mapping (SLAM) approaches, offer different trade-offs in terms of achievable accuracy, infrastructure complexity, cost, and robustness to occlusion and interference [2] . Sensor fusion frameworks that combine vision, inertial, and UWB data can improve robustness and deliver cost–accuracy trade-offs, as demonstrated in large-scale warehouse deployments. However, their performance and economic viability remain highly dependent on environmentspecific factors [3] . In addition, this study also treats localisation as the backbone for real-time asset monitoring, allowing ground support equipment, tooling, and spares to be tracked seamlessly in the bay. On the opposite scale, artefact localisation is addressed, with an exploration of how sensing can facilitate the identification of surface defects and other critical features on the airframe. The framing of these macroscopic and microscopic needs together sets the stage for the optimisation framework, comparative experiments, and cost analyses developed in the remainder of the paper.  \nDespite technical advances, a notable lack of comprehensive, real-world techno-economic analyses remains, specifically focused on aircraft hangar deployments. The available literature provides only partial benchmarking or component-level comparisons for individual or hybrid localisation modalities [4, 5], with  \n∗[Corresponding author: a.plastropoulos@cranfield.ac.uk](Corresponding author: a.plastropoulos@cranfield.ac.uk)  \n1Centre for Assured And Connected Autonomy, Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, MK43 0AL, United Kingdom  \n2Department of Aeronautics and Astronautics, School of Engineering, Uni","cbCaifkLsHKgHPF0","https://ap.wps.com/l/cbCaifkLsHKgHPF0","pdf",10744242,29,"English","# Abstract\n# 1 Introduction","[{\"question\":\"Why is localisation considered challenging in MRO hangars?\",\"answer\":\"MRO hangars create a GPS-denied environment with tall ceilings, metallic structures, extensive multipath effects, and stringent operational constraints that together make localisation uniquely difficult.\"},{\"question\":\"Which sensing approaches does the paper benchmark, and for what scenarios?\",\"answer\":\"It benchmarks MoCap, UWB, and a ceiling-mounted camera network for robot localisation, asset tracking, and surface defect detection within a 40 × 50 m hangar bay.\"},{\"question\":\"What is the dual-layer optimisation framework introduced in the study?\",\"answer\":\"The framework couples market-based camera-lens selection with an optimisation solver and produces camera layouts that minimise hardware while still meeting accuracy targets.\"}]","Techno-Economic analysis for Smart Hangar inspection operations through Sensing and Localisation at scale | PDF",73]