Egocentric Constraint Corridor: Deep Reinforcement Learning for Fixed-Wing UAV Navigation in Vertically Constrained Airspace

Fixed-wing UAVs operating in long-range missions often fly through airspace subject to heterogeneous multi-source constraints that vertically compress the flyable space into a constraint corridor of continuously varying thickness. Conventional path planning methods incur high online computational costs in such scenarios. Deep reinforcement learning can generate reactive decisions from local observations, yet existing approaches predominantly target multirotor obstacle avoidance and rely on observations designed for discrete obstacles, lacking a unified representation for corridor constraints. Moreover, constraint conditions vary across mission scenarios, demanding cross-scenario policy generalization. This paper proposes the Egocentric Constraint Corridor (ECC), which fuses multi-source constraints into upper and lower boundary surfaces defining the corridor, then egocentrically encodes the surrounding corridor relative to the vehicle into a margin field serving as structured policy input. A deep reinforcement learning framework built on ECC is trained end-to-end, with its multi-branch network and composite reward function following from the structure of the corridor encoding. Experiments show that ECC-DRL achieves path efficiency approaching that of globally informed A*, and that it is the only one of the compared methods that computes its decisions online within the decision interval. Ablation studies confirm the margin field is necessary for reliable navigation, and the ECC encoding enables zero-shot transfer to scenarios with unseen terrains and radar deployments without retraining. Hardware-in-the-loop experiments on an embedded platform verify real-time closed-loop feasibility.