Time-optimal planning for quadrotor waypoint flight

Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor’s actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world’s largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.

A Robust Hybrid Control for Autonomous Flying Robots in an Uncertain and Disturbed Environment

With the aim of efficiently achieving complex trajectory tracking missions in the presence of model uncertainties and exogenous disturbances, this paper proposes a robust hybrid control for the orientation and position of flying robots by adopting insights from sliding mode, geometric tracking, and nonlinear feedback control strategies. Various retrofits are implemented to the composite control scheme in order to tackle the system uncertainties, eliminate the chattering effects, and enhance the trajectory tracking performance. The convergence and stability analysis demonstrated the asymptotic stability of the proposed control algorithm. To reveal the promising performance of the developed control schemes, a qualitative comparative analysis of different proposed control approaches is performed. The comparative analysis examines highly maneuverable trajectories for various tracking scenarios in the presence of uncertain disturbances. The simulation results demonstrated the versatility, robustness, and convergence of the developed control laws that allow autonomous flying robots to effectively perform agile maneuvers.

Signal-Based Self-Organization of a Chain of UAVs for Subterranean Exploration

Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measurement of the signal quality between two successive robots and an estimate of the ground speed based on an optic flow sensor. It leverages a distributed policy for each UAV and a Kalman filter to get reliable estimates of the signal quality. We evaluate our approach formally and in simulation, and we describe experimental results with a chain of 3 real miniature quadrotors (12 by 12 cm) and a base station.