Supervisory Control of Multirotor Vehicles in Challenging Conditions using Inertial Measurements

We consider the problem where a supervisor or remote pilot provides a real-time linear velocity reference to a multirotor aerial robot; either through a traditional remote control handset, a modern haptic interface, or semi-autonomous guidance control system. In all such cases, the goal is to servo-control the vehicle's velocity to the set point as quickly and as efficiently as possible. The challenge is to achieve this robustly in the presence of unknown wind disturbances and in situations where the vehicle moves into GPS denied environments (indoors, urban canyons, forests) where estimation of the vehicle's velocity is challenging. These situations include unclutterred environments , poor visibility environments caused by poor lighting and poorly textured visual environments where laser and vision based sensors become unreliable. The approach taken is to develop a coupled non-linear complementary velocity aided attitude filter that provides estimates of both the inertial and body-fixed frame linear velocities, as well as the attitude of a multirotor aerial vehicle, that functions effectively even when only the inertial measurement unit (IMU) and barometric sensor measurements are available. When full inertial velocity measurements are available (from GPS, Vicon or a vision system), the filter additionally estimates the external wind speed. In this paper we formally present the proposed filter along with experimental results and a comparison of the filter to recent results in the literature and in situations where inertial reference frame velocities are available intermittently. The proposed filter is computationally simple to implement and easy to calibrate, tune and provides an excellent base level functionality for modern multirotor aerial robotic systems that will be required to function robustly in a variety of environments.