This paper addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods relied on accurate knowledge of the environment, including the gap's pose and size. In contrast, we integrate onboard sensing and detect the gap from a single onboard camera. The training problem is challenging for two reasons: a precise and robust whole-body planning and control policy is required for variable-tilted and narrow gaps, and an effective Sim2Real method is needed to successfully conduct real-world experiments. To this end, we propose a learning framework for agile gap traversal flight, which successfully trains the vehicle to traverse through the center of the gap at an approximate attitude to the gap with aggressive tilted angles. The policy trained only in a simulation environment can be transferred into different domains with fine-tuning while maintaining the success rate. Our proposed framework, which integrates onboard sensing and a neural network controller, achieves a success rate of 84.51% in real-world experiments, with gap orientations up to 60deg. To the best of our knowledge, this is the first paper that performs the learning-based variable-tilted narrow gap traversal flight in the real world, without prior knowledge of the environment.

翻译：本文针对四旋翼飞行器在未知、倾斜且狭窄的缝隙中进行穿越的问题，采用深度强化学习（DRL）方法展开研究。以往基于学习的方法依赖于对环境（包括缝隙姿态与尺寸）的精确感知。相比之下，本文融合机载感知技术，通过单个机载摄像头检测缝隙。训练过程面临两大挑战：一是需要为变倾斜角度的窄缝制定精确且鲁棒的全身规划与控制策略，二是需要有效的虚拟到现实（Sim2Real）方法以成功开展实机实验。为此，我们提出了一种针对敏捷缝隙穿越飞行的学习框架，成功训练飞行器以接近缝隙中心的姿态、在较大倾斜角度下完成穿越。该策略仅在仿真环境中训练，通过微调即可迁移至不同场景，同时保持穿越成功率。所提出的框架整合了机载感知与神经网络控制器，在实机实验中实现了84.51%的成功率，可应对高达60°的缝隙倾斜角度。据我们所知，这是首篇在无需环境先验知识的情况下，于真实环境中实现基于学习的变角度窄缝穿越飞行的论文。