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：本文研究了基于深度强化学习的四旋翼无人机在未知倾斜窄缝中的穿越问题。以往基于学习的方法依赖于对环境的精确认知，包括缝隙的位姿和尺寸。相比之下，我们集成了机载感知机制，通过单个机载摄像头检测缝隙。训练面临两大挑战：需要为可变倾斜窄缝设计精确且鲁棒的全身规划与控制策略，以及需要有效的Sim2Real方法以成功开展真实实验。为此，我们提出一种敏捷窄缝穿越飞行学习框架，成功训练飞行器以接近缝隙姿态的大倾斜角度穿越其中心。仅在仿真环境中训练的策略可通过微调迁移至不同域，同时保持成功率。集成机载感知与神经网络控制器的框架在真实实验中实现了84.51%的穿越成功率，缝隙倾斜角最高达60度。据我们所知，这是首次在无环境先验知识条件下，基于学习方法实现真实世界可变倾斜窄缝穿越飞行的研究。