A self-learning state-aware rescheduling approach for robot-aided aero-engines fleet inspection

Abstract

With the concept development of aircraft intelligent propulsion system, the use of intelligent robot swarms for aero-engines fleet inspection has become a trend. However, the uncertainty of aircraft arrival brings difficulties to the scheduling of inspection robots. Considering the effect of uncertainty on system state, a state-aware rescheduling approach is proposed for robot-aided aero-engines fleet inspection. In which, a system state evaluation considering both adequacy of inspection resources and urgency of inspection tasks is proposed. Reinforcement learning and metaheuristic algorithm are used to implement the self-learning rescheduling and scheduling plan solving respectively. Experimental results show the effectiveness of the proposed approach, which can overcome the short-sightedness of metaheuristic algorithm and achieve long-term improvement of the scheduling objectives.