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About

Xinyi Song is a researcher at the forefront of intelligent automation in aerospace maintenance, specializing in adaptive scheduling and multi-robot path planning for aero-engine fleet operations. Her work addresses critical challenges in aircraft Maintenance, Repair, and Operation (MRO), particularly the unpredictability and specialization of aero-engine fault detection. Song’s most notable contribution is a self-learning, state-aware rescheduling approach for robot-aided fleet inspection (2022, 7 citations), which enhances real-time adaptability during inspections. She also developed a classified situations-oriented adaptive scheduling method for robot-aided fault detection (2021, 3 citations), directly supporting the aviation industry’s growing demand for efficient MRO solutions. Additionally, her path planning method incorporating a resource reuse strategy (2022, 2 citations) introduces a multi-robot formation reuse approach for inspection resource delivery, optimizing fleet maintenance logistics. By integrating resource reusability and adaptive scheduling, Song’s research advances the practical deployment of robotic systems in high-stakes aerospace environments. Her work is particularly relevant as industry leaders like Rolls-Royce explore the future of automated maintenance, positioning Song’s contributions as foundational to next-generation, cost-effective aero-engine fleet management.