Safer UAV Piloting: A Robust SenseandAvoid Solution for Remotely Piloted Quadrotor UAVs in Complex Environments Current commercial UAVs are to a large extent remotely piloted by amateur human pilots. Due to lack of teleoperation experience or skills, they often drive the UAVs into collision. Therefore, in order to ensure safety of the UAV as well as its surroundings, it is necessary for the UAV to boast the capability of detecting emergency situation and acting on its own when facing imminent threat. However, the majority of UAVs currently available in the market are not equipped with such capability. To fill in the gap, in this paper we present a complete senseandavoid solution for assisting unskilled pilots in ensuring a safe flight. Particularly, we propose a novel nonlinear vehicle control system which takes into account of sensor characteristics, an emergency evaluation policy and a novel optimizationbased avoidance control strategy. The effectiveness of the proposed approach is demonstrated and validated in simulation with multiple moving objects.

An Autonomous Quadrotor System for Robust HighSpeed Flight Through Cluttered Environments Without GPS Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other applications require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while highspeed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two widefieldofview cameras for visualinertial motion tracking and relocalization to a prior map. Collisionfree trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotorspecific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 ms and vertical flights at up to 3.1 ms compared to the stateoftheart, and 3) demonstrate our system tracking complex, aggressive, trajectories.