Tree trunk detection system using LiDAR for a semi-autonomous tree felling robot

Kolb, Alexander; Meaclem, Christopher V.; Chen, Xiaoqi; Parker, Richard; Gutschmidt, Stefanie; Milne, Bart

doi:10.1109/iciea.2015.7334089

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Meaclem et al investigated a biped mechanism of a felling machine that climbs between trees using two legs like a monkey [11]. Kolb et al proposed a tree trunk detection system for a Open Access *Correspondence: tenkai@iwate-u.ac.jpsemi-autonomous tree felling robot [10]. Gui et al developed an anti falling mechanism for tree pruning robots [3].…”

Section: Introductionmentioning

confidence: 99%

Safe tree felling using expansion agent

Kim

Abe²,

Numakura

et al. 2017

Robomech J

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We discuss about safe tree felling methods. In previous work, tree felling robots that were equipped with cutting instruments have been investigated in order to keep workers' safety. However, these robots may take damage when trees fall down against them. We propose a tree felling framework that maintains the workers and robots both in safe. In the proposed framework, a robot inserts a slow-acting expansion agent at the trunk of a tree. While the agent is expanding, the workers and robots escape from the tree. Also, we propose two drilling methods that are able to realize the proposed framework. In the experiments, conventional drilling methods that have been used for rocks did not divide logs. On the other hand, the proposed methods realized the divisions without any crack in the product side of them. The proposed methods were effective against standing trees too.

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Section: Introductionmentioning

confidence: 99%

Safe tree felling using expansion agent

Kim

Abe²,

Numakura

et al. 2017

Robomech J

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Tree Trunk and Obstacle Detection in Apple Orchard Based on Improved YOLOv5s Model

Zhao

Shi

et al. 2022

Agronomy

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In this paper, we propose a tree trunk and obstacle detection method in a semistructured apple orchard environment based on improved YOLOv5s, with an aim to improve the real-time detection performance. The improvement includes using the K-means clustering algorithm to calculate anchor frame and adding the Squeeze-and-Excitation module and 10% pruning operation to ensure both detection accuracy and speed. Images of apple orchards in different seasons and under different light conditions are collected to better simulate the actual operating environment. The Gradient-weighted Class Activation Map technology is used to visualize the performance of YOLOv5s network with and without improvement to increase interpretability of improved network on detection accuracy. The detected tree trunk can then be used to calculate the traveling route of an orchard carrier platform, where the centroid coordinates of the identified trunk anchor are fitted by the least square method to obtain the endpoint of the next time traveling rout. The mean average precision values of the proposed model in spring, summer, autumn, and winter were 95.61%, 98.37%, 96.53%, and 89.61%, respectively. The model size of the improved model is reduced by 13.6 MB, and the accuracy and average accuracy on the test set are increased by 5.60% and 1.30%, respectively. The average detection time is 33 ms, which meets the requirements of real-time detection of an orchard carrier platform.

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Tree trunk detection system using LiDAR for a semi-autonomous tree felling robot

Cited by 2 publications

References 8 publications

Safe tree felling using expansion agent

Safe tree felling using expansion agent

Tree Trunk and Obstacle Detection in Apple Orchard Based on Improved YOLOv5s Model

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