论文标题
学会协助无人机着陆
Learning to Assist Drone Landings
论文作者
论文摘要
无人驾驶汽车（UAV）通常用于驾驶危险的地形，但是很难驾驶。由于复杂的输入输出映射方案，有限的感知，复杂的系统动力学以及保持安全的操作距离的需求，新手飞行员在填充障碍物环境中执行安全着陆时遇到困难。在这项工作中，我们提出了一种共同的自治方法，该方法有助于新手飞行员在几个高架平台之一上进行安全着陆，其熟练程度等于或高于经验丰富的飞行员。我们的方法包括两个模块，一个感知模块和一个策略模块。感知模块将高维度RGB-D图像压缩到经过跨模式变异自动编码器训练的潜在矢量中。该策略模块提供了使用增强算法TD3训练的辅助控制输入。我们进行了一项用户研究（n = 33），参与者有或不使用助手，降落了模拟的无人机。尽管助手不知道目标平台，但所有技能水平的参与者都能够在协助任务的同时胜过经验丰富的参与者。
Unmanned aerial vehicles (UAVs) are often used for navigating dangerous terrains, however they are difficult to pilot. Due to complex input-output mapping schemes, limited perception, the complex system dynamics and the need to maintain a safe operation distance, novice pilots experience difficulties in performing safe landings in obstacle filled environments. In this work we propose a shared autonomy approach that assists novice pilots to perform safe landings on one of several elevated platforms at a proficiency equal to or greater than experienced pilots. Our approach consists of two modules, a perceptual module and a policy module. The perceptual module compresses high dimensionality RGB-D images into a latent vector trained with a cross-modal variational auto-encoder. The policy module provides assistive control inputs trained with the reinforcement algorithm TD3. We conduct a user study (n=33) where participants land a simulated drone with and without the use of the assistant. Despite the goal platform not being known to the assistant, participants of all skill levels were able to outperform experienced participants while assisted in the task.
