The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.
翻译:无人驾驶航空飞行器(无人驾驶飞行器)领域在过去几年中已经高度成熟,因此,将封闭实验室的这种平台带到与人类的日常互动对于无人驾驶飞行器的商业化十分重要。本文所关注的人类-无人驾驶航空飞行器(无人驾驶飞行器)的一个特定情景是有效载荷移交计划,无人驾驶航空飞行器应人的请求将有效载荷交给人。在这一范围中,本文件展示了一种新的实时人-无人驾驶航空飞行器互动探测方法,即开发基于神经网络的长期短期内存(LSTM),以探测由人类互动动态产生的状态特征。介绍了新的数据预处理技术;这种技术利用了培训和测试无人驾驶飞行器的估计过程参数,以建立动态变异测试数据。拟议的检测算法是轻量的,因此可以在架子上使用无人驾驶航空飞行器平台实时部署;此外,这完全取决于任何古典无人驾驶航空平台上的惯性和位置测量。拟议方法展示了多管无人驾驶飞行器和人类之间的有效载荷移交任务。在实际测试96年的飞行实验中,即使进行了两次测测测测测,也没有进行实际飞行和测试数据。