拡散モデルによる複雑環境でのアクロバティック飛行の自動生成
Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models
拡散モデルを用いて、複雑な環境でのアクロバティック飛行軌道を自動生成するフレームワークを提案。機動をプリミティブに分解し、ウェイポイントや制約を条件として与えることで、ユーザー編集可能な軌道生成を実現した。
著者: Yuhang Zhong, Anke Zhao, Tianyue Wu, Tingrui Zhang, Fei Gao
分類: cs.RO
原文アブストラクト
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.
