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

TL;DR

This paper presents a vision-based method for autonomous drones to identify optimal perching sites on trees by analyzing branch shape, size, and orientation using image processing techniques, achieving 76% success on urban tree images.

Contribution

The study introduces a novel vision-guided approach for selecting suitable tree branches for drone perching, incorporating shape and structural analysis to improve landing accuracy.

Findings

Successfully identified feasible perching targets in 76% of cases

Utilized image processing algorithms including segmentation and morphology

Established a foundation for future enhancements with depth and attitude sensors

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.