Locally guided reinforcement learning for autonomous dispatching of carrier-based aircraft

The limited deck space and highly dynamic environment pose significant challenges for autonomous dispatching of carrier-based aircraft. While existing reinforcement learning-based automatic parking techniques offer novel technological insights for autonomous carrier aircraft dispatching, these methods encounter non-convergence issues when directly applied to dynamic environments with constrained aircraft postures. To address this limitation, this paper proposes a locally guided reinforcement learning approach for carrier aircraft autonomous dispatching. The method introduces dual reward mechanisms: a reference trajectory-based local target state reward and a local state grid reward near the dispatching endpoint. These mechanisms effectively guide the learning process, preventing both local optima entrapment and convergence failure during training, thereby significantly enhancing the success rate of autonomous carrier aircraft dispatching. Experimental results demonstrate that the proposed approach outperforms conventional autonomous dispatching methods in terms of both success rate and operational safety. The method’s effectiveness has been validated in various mission scenarios and different carrier aircraft configurations.