Multi-task missions for unmanned aerial vehicles (UAVs) involving inspection and landing tasks are challenging for novice pilots due to the difficulties associated with depth perception and the control interface. We propose a shared autonomy system, alongside supplementary information displays, to assist pilots to successfully complete multi-task missions without any pilot training. Our approach comprises of three modules: (1) a perception module that encodes visual information onto a latent representation, (2) a policy module that augments pilot's actions, and (3) an information augmentation module that provides additional information to the pilot. The policy module is trained in simulation with simulated users and transferred to the real world without modification in a user study (n=29), alongside supplementary information schemes including learnt red/green light feedback cues and an augmented reality display. The pilot's intent is unknown to the policy module and is inferred from the pilot's input and UAV's states. The assistant increased task success rate for the landing and inspection tasks from [16.67% & 54.29%] respectively to [95.59% & 96.22%]. With the assistant, inexperienced pilots achieved similar performance to experienced pilots. Red/green light feedback cues reduced the required time by 19.53% and trajectory length by 17.86% for the inspection task, where participants rated it as their preferred condition due to the intuitive interface and providing reassurance. This work demonstrates that simple user models can train shared autonomy systems in simulation, and transfer to physical tasks to estimate user intent and provide effective assistance and information to the pilot.

翻译：针对无人机（UAV）的巡检与着陆等多任务作业，由于深度感知与控制界面的困难，新手驾驶员面临挑战。我们提出一种共享自主系统，辅以信息显示，帮助驾驶员在无任何训练的情况下成功完成多任务作业。该系统由三个模块组成：（1）感知模块，将视觉信息编码为潜在表征；（2）策略模块，增强驾驶员操作；（3）信息增强模块，为驾驶员提供额外信息。策略模块在仿真环境中使用模拟用户进行训练，并在用户研究（n=29）中无需修改直接迁移至现实世界，同时辅以学习型红/绿光反馈提示与增强现实显示等信息方案。驾驶员意图对策略模块未知，需通过驾驶员输入与无人机状态进行推断。辅助系统将着陆与巡检任务的成功率分别从[16.67%与54.29%]提升至[95.59%与96.22%]。在辅助系统下，经验不足的驾驶员达到了与经验丰富驾驶员相近的性能。红/绿光反馈提示使巡检任务所需时间减少19.53%，轨迹长度减少17.86%，参与者因其直观界面与安全感将其评为最偏好条件。本研究证明，简单用户模型可在仿真中训练共享自主系统，并迁移至物理任务以估计用户意图，为驾驶员提供有效辅助与信息。