A commercial aircraft is struck by lightning on average once a year. To rule out possible damage to the aircraft, a time‐consuming visual inspection of the aircraft is carried out by maintenance staff. In order to reduce overall maintenance costs and aircraft on ground time, an autonomous unpiloted aerial vehicle is utilized. The mobile unit allows an easy inspection of the compromised area, by carrying industrial camera technology to digitalize the aircrafts’ surface. So this approach applies and extends classic machine vision as well as machine learning algorithms to automatically detect maintenance‐relevant surface defects in location‐indexed images recorded by the system.
This paper contributes a conceptual CAPabILity-based resource AllocatioN Ontology (CAPILANO). The ontology is tailored as a uniform description of heterogeneous assembly resources and their (combined) capabilities, connected to a capability-based task allocation approach. The intended application of the resulting framework is the identification of suitable assembly resources in Line-less Mobile Assembly Systems (LMAS) and their allocation to assembly tasks, based on a unified and formal description. To date, ontologies in assembly have been limited to querying resources and their capabilities; here, subsequent task allocation is presented as an integral component of a tailored framework. The resulting framework consists of a model of heterogeneous resources and their capabilities in an ontology created in Protégé in OWL, SPARQL-based query-ing, and a consecutive and availability-aware task allocation in Python.<br>
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