Increased autonomy for quadrocopter systems: trajectory generation, fail safe strategies and state estimation
This thesis investigates three specific topics aimed at increasing the autonomy of quadrocopter systems. Such systems have become popular as they provide relatively easy and cheap access to the sky, allowing e.g. novel aerial perspectives, or the transportation of goods. Their potential value to industry is derived in a large part by their autonomy: their ability to safely and reliably execute tasks with minimal human intervention. The three topics investigated in this thesis are trajectory generation, fail safe strategies and novel vehicles, and state estimation. The trajectory generation algorithms investigated focus on achieving agile manoeuvres at low computational cost. Two approaches are presented, of which the first can generate and verify a candidate trajectory in approximately one microsecond on a normal laptop, achieved by only verifying feasibility after the generation of the motion. The second approach transforms the trajectory generation task into a convex optimisation problem, and then uses modern, highly optimised methods to solve for the trajectory, at the cost of using conservative convex approximations of the feasible inputs. The thesis investigates component failures that would break the global feedback control loop in an indoor aerial robotics testbed, and suggests strategies to mitigate the effects of such a failure. In addition, a detailed analysis into the attitude dynamics of multicopters is presented, from which follow generic conditions for a multicopter to maintain a "relaxed hover" (that is, remaining substantially at one place) This analysis is used to derive controllers with which a quadrocopter can maintain flight despite the complete loss of all but one of its propellers. Similarly, novel vehicles are designed that have fewer than four propellers. The research into state estimation presents two generic methods for state estimation of a dynamic system, where the state includes an attitude. The two methods are extensions of the extended and unscented Kalman filters, and build on existing methods that encode the attitude to be estimated with a redundant representation (the system state vector includes a three element representation, in addition to a separate "reference" attitude) It is shown that, in contrast to existing methods, the methods are shown to keep track of the covariance correctly. Based on the desire to have cheap, reliable, and flexible localisation for flying vehicles, work is also presented that fuses ultra wideband ranging radios with the inertial sensors on a quadrocopter to estimate the quadrocopter's position, velocity, attitude, and angular velocity. This estimator is computationally light weight enough to be run on a standard microcontroller, and the additional sensors are light and small enough to not significantly impact the performance of the quadrocopter to which they are attached.