Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones
This study presents a vision-guided method for autonomous drones to select ideal perching spots on trees. Using image processing algorithms including machine learning, segmentation, and binary morphology, the method evaluates branch width, slope, and curvature rather than simply choosing the nearest branch. Tested on over 10,000 urban tree images, it succeeds for 76% of feasible targets. Future work will incorporate depth perception and attitude sensors.
[2605.15430] Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones
[Submitted on 14 May 2026]
Title:Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones
View a PDF of the paper titled Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones, by Alex Dunnett and 3 other authors
View PDF HTML (experimental)
Abstract:This study demonstrates a method to locate an ideal perch location on a tree for vision-guided autonomous tree-perching drones. Various image processing algorithms, including those used for machine learning, image segmentation and binary image morphology, are implemented to assess the shape and structure of a tree. Rather than identifying the closest available branch, this study builds on vision methods by evaluating the potential of each branch, determining its suitability for perching based on factors such as branch width, slope (angle to the horizontal) and curvature. For a given tree-perching drone and a dataset of more than 10,000 urban tree images taken from February to October in a subtropical and temperate monsoon climate, the proposed method successfully produces a result for 76% of feasible targets. A feasible target defined as a tree where the branch diameters are sufficiently thick and where the available perching space is at least equal to the width of a tendon-driven grasping claw. These successful preliminary results create a foundation from which a number of identified improvements and additional features can be developed to create a generalised method; this will involve the incorporation of supplementary data from depth perception and attitude sensors to enhance the branch assessment.
Comments: Work in progress version accepted to the Recent Advances in Robotic Perception for Forestry
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.15430 [cs.RO]
(or arXiv:2605.15430v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.15430
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bahadir Kocer [view email] [v1] Thu, 14 May 2026 21:25:11 UTC (30,543 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones, by Alex Dunnett and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-05
Change to browse by:
cs cs.CV
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)