
The increasing demand for drones and air taxis in advanced aerial mobility (AAM) necessitates highly efficient and error-free maintenance operations, compelling significant technological innovation in this space. Traditional human technicians who rely on sensory-based decisions to detect mechanical defects in drones and air taxis often face challenges of inconsistency and inefficiency, particularly when managing large fleets. Addressing these critical issues