Novice pilots find it difficult to operate and land unmanned aerial vehicles (UAVs), due to the complex UAV dynamics, challenges in depth perception, lack of expertise with the control interface and additional disturbances from the ground effect. Therefore we propose a shared autonomy approach to assist pilots in safely landing a UAV under conditions where depth perception is difficult and safe landing zones are limited. Our approach comprises of two modules: a perception module that encodes information onto a compressed latent representation using two RGB-D cameras and a policy module that is trained with the reinforcement learning algorithm TD3 to discern the pilot's intent and to provide control inputs that augment the user's input to safely land the UAV. The policy module is trained in simulation using a population of simulated users. Simulated users are sampled from a parametric model with four parameters, which model a pilot's tendency to conform to the assistant, proficiency, aggressiveness and speed. We conduct a user study (n = 28) where human participants were tasked with landing a physical UAV on one of several platforms under challenging viewing conditions. The assistant, trained with only simulated user data, improved task success rate from 51.4% to 98.2% despite being unaware of the human participants' goal or the structure of the environment a priori. With the proposed assistant, regardless of prior piloting experience, participants performed with a proficiency greater than the most experienced unassisted participants.

翻译：新手飞行员因无人机复杂的动力学特性、深度感知困难、缺乏操控界面使用经验以及地面效应带来的额外扰动，难以操作和着陆无人航空器（UAV）。为此，我们提出一种共驾方法，在深度感知困难且安全着陆区域有限的条件下辅助飞行员安全着陆无人机。该方法包含两个模块：感知模块利用两个RGB-D摄像头将信息编码至压缩潜表征中；策略模块通过强化学习算法TD3进行训练，以识别飞行员意图并提供控制输入来增强用户操作，从而实现安全着陆。策略模块在仿真环境中使用模拟用户群体进行训练。模拟用户采样自一个四参数模型，该模型刻画了飞行员对辅助系统的依赖倾向、熟练度、激进程度及速度。我们开展了一项包含28名受试者的用户实验，要求参与者在受限视野条件下将实体无人机着陆至多个平台之一。仅使用模拟用户数据训练的辅助系统，在预先未知人类参与者目标或环境结构的情况下，将任务成功率从51.4%提升至98.2%。使用所提出的辅助系统后，无论参与者之前的驾驶经验如何，其操作熟练度均超过未接受辅助的最有经验参与者。