Catch Planner: Catching High-Speed Targets in the Flight

Catching high-speed targets in the flight is a complex and typically highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lack of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called catch planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real-time onboard at
$100\text{ Hz}$
. Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets.