Performing striking aerobatic flight in complex environments demands manual
designs of key maneuvers in advance, which is intricate and time-consuming as
the horizon of the trajectory performed becomes long. This paper presents a
novel framework that leverages diffusion models to automate and scale up
aerobatic trajectory generation. Our key innovation is the decomposition of
complex maneuvers into aerobatic primitives, which are short frame sequences
that act as building blocks, featuring critical aerobatic behaviors for
tractable trajectory synthesis. The model learns aerobatic primitives using
historical trajectory observations as dynamic priors to ensure motion
continuity, with additional conditional inputs (target waypoints and optional
action constraints) integrated to enable user-editable trajectory generation.
During model inference, classifier guidance is incorporated with batch sampling
to achieve obstacle avoidance. Additionally, the generated outcomes are refined
through post-processing with spatial-temporal trajectory optimization to ensure
dynamical feasibility. Extensive simulations and real-world experiments have
validated the key component designs of our method, demonstrating its
feasibility for deploying on real drones to achieve long-horizon aerobatic
flight.

Questo articolo esplora i giri e le loro implicazioni.

Scarica PDF: