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Catching high-speed targets in the flight is a complex and typical highlydynamic task. In this paper, we propose Catch Planner, a planning-with-decisionscheme for catching. For sequential decision making, we propose a policy searchmethod based on deep reinforcement learning. In order to make catching adaptiveand flexible, we propose a trajectory optimization method to jointly optimizethe highly coupled catching time and terminal state while considering thedynamic feasibility and safety. We also propose a flexible constrainttranscription method to catch targets at any reasonable attitude and terminalposition bias. The proposed Catch Planner provides a new paradigm for thecombination of learning and planning and is integrated on the quadrotordesigned by ourselves, which runs at 100hz on the onboard computer. Extensiveexperiments are carried out in real and simulated scenes to verify therobustness of the proposed method and its expansibility when facing a varietyof high-speed flying targets.

Catch Planner: Catching High-Speed Targets in the Flight