機上視覚による動的環境での自律飛行
Autonomous Flights in Dynamic Environments with Onboard Vision
深度画像から動的・静的障害物を分類し、動的物体を楕円体でモデル化してキノダイナミック探索と勾配最適化でミリ秒単位の回避計画を行うクアッドロータ自律飛行システムを実機で検証した。
著者: Yingjian Wang, Jialin Ji, Qianhao Wang, Chao Xu, Fei Gao
分類: cs.RO
原文アブストラクト
In this paper, we introduce a complete system for autonomous flight of quadrotors in dynamic environments with onboard sensing. Extended from existing work, we develop an occlusion-aware dynamic perception method based on depth images, which classifies obstacles as dynamic and static. For representing generic dynamic environment, we model dynamic objects with moving ellipsoids and fuse static ones into an occupancy grid map. To achieve dynamic avoidance, we design a planning method composed of modified kinodynamic path searching and gradient-based optimization. The method leverages manually constructed gradients without maintaining a signed distance field (SDF), making the planning procedure finished in milliseconds. We integrate the above methods into a customized quadrotor system and thoroughly test it in realworld experiments, verifying its effective collision avoidance in dynamic environments.
