  This paper presents Deep-PANTHER, a learning-based perception-aware
trajectory planner for unmanned aerial vehicles (UAVs) in dynamic environments.
Given the current state of the UAV, and the predicted trajectory and size of
the obstacle, Deep-PANTHER generates multiple trajectories to avoid a dynamic
obstacle while simultaneously maximizing its presence in the field of view
(FOV) of the onboard camera. To obtain a computationally tractable real-time
solution, imitation learning is leveraged to train a Deep-PANTHER policy using
demonstrations provided by a multimodal optimization-based expert. Extensive
simulations show replanning times that are two orders of magnitude faster than
the optimization-based expert, while achieving a similar cost. By ensuring that
each expert trajectory is assigned to one distinct student trajectory in the
loss function, Deep-PANTHER can also capture the multimodality of the problem
and achieve a mean squared error (MSE) loss with respect to the expert that is
up to 18 times smaller than state-of-the-art (Relaxed) Winner-Takes-All
approaches. Deep-PANTHER is also shown to generalize well to obstacle
trajectories that differ from the ones used in training.
