Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908943795617792 |
|---|---|
| author | Zhang, Linzuo Hu, Yu Yu, Feng Deng, Yang Yu, Wenxian Zou, Danping |
| author_facet | Zhang, Linzuo Hu, Yu Yu, Feng Deng, Yang Yu, Wenxian Zou, Danping |
| contents | -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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02779 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation Zhang, Linzuo Hu, Yu Yu, Feng Deng, Yang Yu, Wenxian Zou, Danping Robotics -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. |
| title | Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.02779 |