Controllability and Design of Unmanned Multirotor Aircraft Robust to Rotor Failure

A new design method for multi-rotor aircraft with distributed electric propulsion is presented to ensure a property of robustness against rotor failure from the control perspective. Based on the concept of null controllability, a quality measure is derived to evaluate and quantify the performance of a given design with the consideration of rotor failure. An optimization problem whose cost function is based on the quality measure is formulated and its optimal solution identifies a set of optimal design parameters that maximizes an aircraft’s ability to control its attitude and hence its position. The effectiveness of the proposed design procedure is validated through the results of experimentation with the Autonomous Flying Ambulance model being developed at Caltech’s Center for Autonomous Systems and Technologies

Exploiting photometric information for planning under uncertainty

Vision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene’s visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no pre-computed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot

Adaptive Robust Attitude Controller Design for a Quadrotor Platform

This paper includes attitude controller design ideas for a quadrotor platform which can be regarded as an exceptionally agile flying robot with highly non-linear and unstable features in flight dynamics. The quadrotor poses severe problems in characterizing the dynamics especially when performing high-speed manoeuvres. These facts cause the quadrotor not to lose its popularity as a compelling tool among avid researchers who endeavour to realize various controller ideas. The procedure in this paper is initiated with the construction of the system model and the verification of this phase relying on the characteristics of the test bed. With the aid of sensors on the off-the-shelf platform, the controllers are designed to enact tracking of the reference commands that contain the desired trajectories and attitudes. The controller methods highlighted in this research are non-linear dynamic inversion, model reference adaptive control and integral back-stepping technique. The trade-off between performance and robustness is investigated as well. The responses of the system to the impacts of the existence of uncertain parameters, unmatched uncertainties or disturbances are exceptional means to judge how robust the controller is. An overview of the cases with parametric uncertainty and the existence of noise, therefore, find its place as a section within this work. This sketch grades the controller options while putting forward the advantage of adaptation. Besides, by employing correction approaches, the advancement of the adaptive controller in terms of robustness is examined where dead zone implementation, parameter bounding, e-, and σ-modifications are exploited. The motivation behind this research is to produce persistent state controllers to lay the first stone for more complex algorithm structures such as autonomous flight phases, obstacle avoidance and way-point targeting. The future work of this study is the justification of the reliability of the methodologies used and the results attained from the simulations through experiments