In this article, we consider the problem of multirotor flying robots physically interacting with the environment under influence of wind. The results are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches acting on the robot based on real-world data, without the need for dedicated sensors. For this purpose, we investigated two model-based techniques for discriminating between aerodynamic and interaction forces. The first technique is based on aerodynamic and contact torque models, and uses the external force to estimate wind speed. Contacts are then detected based on the residual between estimated external torque and expected (modeled) aerodynamic torque. Upon detecting contact, wind speed is assumed to change very slowly. From the estimated interaction wrench, we are also able to determine the contact location. This is embedded into a particle filter framework to further improve contact location estimation. The second algorithm uses the propeller aerodynamic power and angular speed as measured by the speed controllers to obtain an estimate of the airspeed. An aerodynamics model is then used to determine the aerodynamic wrench. Both methods rely on accurate aerodynamics models. Therefore, we evaluate data-driven and physics-based models as well as offline system identification for flying robots. For obtaining ground-truth data, we performed autonomous flights in a 3D wind tunnel. Using this data, aerodynamic model selection, parameter identification, and discrimination between aerodynamic and contact forces could be performed. Finally, the developed methods could serve as useful estimators for interaction control schemes with simultaneous compensation of wind disturbances.

中文翻译：

---

空中机器人的同时接触和气动力估计（s-CAFE）

在本文中，我们考虑了多旋翼飞行机器人在风的影响下与环境进行物理交互的问题。结果是第一个基于真实世界数据同时在线估计作用在机器人上的接触和空气动力学扳手的算法，而无需专用传感器。为此，我们研究了两种基于模型的技术来区分空气动力和相互作用力。第一种技术基于空气动力学和接触扭矩模型，并使用外力来估计风速。然后基于估计的外部扭矩和预期（建模）空气动力扭矩之间的残差检测接触。在检测到接触时，假定风速变化非常缓慢。从估计的交互扳手，我们还能够确定联系位置。这被嵌入到粒子过滤器框架中以进一步改进接触位置估计。第二种算法使用由速度控制器测量的螺旋桨气动功率和角速度来估计空速。然后使用空气动力学模型来确定空气动力学扳手。这两种方法都依赖于精确的空气动力学模型。因此，我们评估数据驱动和基于物理的模型以及飞行机器人的离线系统识别。为了获得地面实况数据，我们在 3D 风洞中进行了自主飞行。使用这些数据，可以执行空气动力学模型选择、参数识别以及空气动力学和接触力之间的区分。最后，