arXiv cs.AI· Marco S. Tayar, Felipe Tommaselli, Gianluca Capezzuto, Pedro Antonio Rabelo Saraiva, Pedro H. V. de Freitas, Lucas Kido, Guilherme Sonego, Ricardo V. Godoy, Marcelo Becker·· 6 小时前AI 评分42
VLN on the Fly：面向空中机器人的机载视觉语言导航栈
VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots
AI 导读
研究者提出 VLN on the Fly，一个在四旋翼无人机上完全机载运行的视觉语言导航栈，将指令定位、规划与控制保持为独立可检查的阶段：量化 VLM 定位指令到图像单元格，深度信息生成 3D 目标，B-spline 规划器输出可行轨迹，再由预训练强化学习策略跟踪执行。
正文 · 原文
Abstract:Running vision-language navigation fully onboard an aerial robot is hard, since grounding, planning, and control must share limited compute and a single-stage error is difficult to isolate in flight. End-to-end aerial policies fuse these stages into one network, giving up the observability and safety checks a modular stack keeps available. We propose VLN on the Fly, an onboard stack that keeps grounding, planning, and control as separate, inspectable stages. A quantized VLM grounds an instruction to a coarse image cell, depth lifts it to a 3D goal, a fast B-spline planner returns a feasible trajectory, and a pretrained reinforcement learning policy tracks it to motor commands across quadrotors. Across 15 onboard flights over three everyday referents in a controlled indoor volume, the stack reaches the target in 13 of 15 trials with 5.72 cm mean goal error and 39.3% average GPU utilization. In 6 additional cluttered-environment trials, the stack tracks collision-free trajectories under onboard perception gating.
| Comments: | 7 pages, 5 figures. Project page: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2609.20191 [cs.RO] |
| (or arXiv:2609.20191v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20191 arXiv-issued DOI via DataCite |
Submission history
From: Gianluca Capezzuto [view email]
[v1]
Fri, 7 Aug 2026 20:34:33 UTC (7,770 KB)
[v2]
Sun, 25 Oct 2026 00:46:44 UTC (7,772 KB)
来源：arXiv cs.AI · arxiv.org