微分可能シミュレーションによる不規則な隙間を通過するUAVの視覚ベースエンドツーエンド学習
Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
深度画像から制御指令へのエンドツーエンド学習で、不規則な隙間を自律的に通過するドローンの制御を実現した論文。
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著者: Linzuo Zhang, Yu Hu, Feng Yu, Yang Deng, Wenxian Yu, Danping Zou
分類: cs.RO
原文アブストラクト
-Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient operator, and a Bimodal Initialization Distribution to achieve stable traversal through consecutive gaps. Two auxiliary prediction modules-a gap-crossing success classifier and a traversability predictor-further enhance continuous navigation and safety. Extensive simulation and real-world experiments demonstrate the approach's effectiveness, generalization capability, and practical robustness.
