  This paper presents PANTHER, a real-time perception-aware (PA) trajectory
planner for multirotor-UAVs (Unmanned Aerial Vehicles) in dynamic environments.
PANTHER plans trajectories that avoid dynamic obstacles while also keeping them
in the sensor field of view (FOV) and minimizing the blur to aid in object
tracking. The rotation and translation of the UAV are jointly optimized, which
allows PANTHER to fully exploit the differential flatness of multirotors to
maximize the PA objective. Real-time performance is achieved by implicitly
imposing the underactuated dynamics of the UAV through the Hopf fibration.
PANTHER is able to keep the obstacles inside the FOV 7.9 and 1.5 times more
than non-PA approaches and PA approaches that decouple translation and yaw,
respectively. The projected velocity (and hence the blur) is reduced by 18% and
34%, respectively. This leads to average success rates three times larger than
state-of-the-art approaches in multi-obstacle avoidance scenarios. The MINVO
basis is used to impose low-conservative collision avoidance constraints in
position and velocity space. Finally, extensive hardware experiments in unknown
dynamic environments with all the computation running onboard are presented,
with velocities of up to 5.8 m/s, and with relative velocities (with respect to
the obstacles) of up to 6.3 m/s. The only sensors used are an IMU, a
forward-facing depth camera, and a downward-facing monocular camera.
