  Catching high-speed targets in the flight is a complex and typical highly
dynamic task. In this paper, we propose Catch Planner, a planning-with-decision
scheme for catching. For sequential decision making, we propose a policy search
method based on deep reinforcement learning. In order to make catching adaptive
and flexible, we propose a trajectory optimization method to jointly optimize
the highly coupled catching time and terminal state while considering the
dynamic feasibility and safety. We also propose a flexible constraint
transcription method to catch targets at any reasonable attitude and terminal
position bias. The proposed Catch Planner provides a new paradigm for the
combination of learning and planning and is integrated on the quadrotor
designed by ourselves, which runs at 100hz on the onboard computer. Extensive
experiments are carried out in real and simulated scenes to verify the
robustness of the proposed method and its expansibility when facing a variety
of high-speed flying targets.
