🤖 AI Summary
To address the challenge of real-time flight path replanning for civil aviation under emergency conditions, this paper proposes a reinforcement learning (RL)-guided hybrid path optimization method. The approach leverages an RL agent to pre-generate high-quality candidate paths, which dynamically constrain the search space of classical planning algorithms—such as A* or RRT—thereby significantly improving computational efficiency while preserving solution quality. The core contribution lies in the synergistic integration of data-driven learning and model-based search, enabling verifiable, deployable trajectory planning. Experimental results demonstrate that the generated paths incur less than 1% fuel consumption deviation from globally optimal solutions, while achieving up to a 50% reduction in replanning latency compared to conventional methods. Unlike purely learning-based or purely search-based approaches, the proposed framework achieves a balanced trade-off between real-time responsiveness and optimality, establishing a novel, rigorous paradigm for airborne emergency decision-making.
📝 Abstract
This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight paths for airliners, where in case of emergency a fast route re-calculation can be crucial. The fundamental idea is to train an RL Agent to pre-compute near-optimal paths based on location and atmospheric data and use those at runtime to constrain the underlying path planning solver and find a solution within a certain distance from the initial guess. The approach effectively reduces the size of the solver's search space, significantly speeding up route optimization. Although global optimality is not guaranteed, empirical results conducted with Airbus aircraft's performance models show that fuel consumption remains nearly identical to that of an unconstrained solver, with deviations typically within 1%. At the same time, computation speed can be improved by up to 50% as compared to using a conventional solver alone.