Supervisory Control of Multirotor Vehicles in Challenging Conditions Using Inertial Measurements

We consider the problem in which 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 in which the vehicle moves into global position system (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 nonlinear 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 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 in which inertial reference frame velocities are available intermittently. The proposed filter is computationally simple to implement, easy to calibrate and 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.