Where to Perch in a Tree: Vision-Guidance for Tree-Grasping Drones

2026-05-14

AI Summary

This study addresses the challenge of precise perching for arboreal drones within complex tree structures by proposing a vision-guided perching site selection method based on multidimensional geometric features. The approach integrates image segmentation, morphological processing, and machine learning to jointly evaluate critical geometric attributes of branches—including width, inclination angle, and curvature—thereby overcoming the limitations of conventional “nearest-available” perching strategies. For the first time, it establishes a generalizable framework for assessing perching suitability tailored to urban trees. Validated on a dataset of over 10,000 urban tree images, the method successfully generated viable perching locations for 76% of feasible targets, significantly enhancing the reliability and adaptability of perching decisions.

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.

Problem

• tree-perching drones
• vision-guidance
• perch location
• branch suitability
• autonomous perching

Innovation

• vision-guided perching
• tree structure assessment
• autonomous drone
• image segmentation
• branch suitability evaluation