Automatic Generation of Aerobatic Flight in Complex Environments via Diffusion Models

2025-04-21

AI Summary

Existing trajectory generation methods for long-duration aerobatic flight in complex environments suffer from heavy reliance on manual design, poor generalizability, and high computational cost. To address these limitations, this paper proposes a fully automated trajectory generation framework. Our method decouples aerobatic maneuvers into learnable aerodynamic primitives and integrates dynamic prior modeling with conditional diffusion-based generation. Classifier-guided sampling enables real-time user intent editing and obstacle avoidance, while spatiotemporal trajectory optimization combined with history-driven dynamic modeling enhances motion plausibility and controllability. Extensive evaluations in simulation and on real quadcopter platforms demonstrate that the framework efficiently generates high-dynamic, long-horizon (>30 s), collision-free aerobatic trajectories. Quantitative and qualitative comparisons show significant improvements over conventional planning and end-to-end learning approaches in terms of trajectory feasibility, dynamism, and adaptability to user specifications and environmental constraints.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automates aerobatic trajectory generation in complex environments
• Decomposes maneuvers into primitives for tractable synthesis
• Ensures obstacle avoidance and dynamical feasibility

Innovation

• Leverages diffusion models for automated trajectory generation
• Decomposes maneuvers into aerobatic primitives for synthesis
• Uses classifier guidance for obstacle avoidance