论文标题
触摸风：同时在多局部上传感的气流，拖动和互动
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
论文作者
论文摘要
微型航空车（MAV）的干扰估计对于鲁棒性和安全性至关重要。在本文中，我们使用以生物风格的气流传感器来测量作用在MAV上的气流，并将这些信息融合在无原来的卡尔曼过滤器（UKF）中，以同时估计三维风矢量，阻力矢量，阻力力和其他相互作用力和其他相互作用力（例如，由于与人的碰撞，与人类相互作用，对Robot的相互作用）。为此，我们介绍并比较了完全基于模型的基于模型的策略。基于模型的方法考虑了MAV和气流传感器动力学及其与风的相互作用，而基于深度学习的策略则使用较长的短期记忆（LSTM）神经网络来获得对相对气流的估计，然后将其融合在建议的过滤器中。我们在硬件实验中验证了我们的方法，表明我们可以准确估计高达4 m/s的相对气流，并且我们可以区分拖放和相互作用力。
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman Filter (UKF) to simultaneously estimate the three-dimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The model-based approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learning-based strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.
