Hedge three-dimensional reconstruction and motion control technology for trimming robot

Abstract

Landscaping is an important way to realize carbon neutralization. The prospect of automatic trimming technology in the horticulture industry has received much attention in recent years. Compared with manual trimming, robots still have a large gap in trimming efficiency and functional integrity. The purpose of this study is to accurately obtain the shape parameters of a hedge by reconstructing its three-dimensional model, enabling the robot to have the complete ability to automate trimming, and improving the efficiency of trimming robot. Firstly, a trimming robot prototype system was constructed by using three-dimensional vision detection technology and autonomous motion control technology. Then, we studied the adaptive template matching method which was used for hedge detection, and the three-dimensional reconstruction method based on curvature feature similarity was used to obtain the position and shape parameters of hedge. We propose an adaptive Ant Colony Optimization trajectory planning method combined with point cloud classification strategy that can improve the efficiency of trimming robot. The results of tests show that the mean absolute value of measurement error of the hand-eye system is 3.7 mm, the mean value of the positioning error of the visual recognition is 2.1 mm, and the mean value of the positioning error of the trimming robot system is 3.8 mm. The trimming robot realized the automatic trimming operation of spherical hedge model and actual hedge in laboratory. During the actual trimming test, it demonstrated an average error of 8.2 mm, and its efficiency and reliability in trimming surpassed manual trimming methods. The research suggests that with the continuous improvement of robot technology, the use of trimming robot system in the horticulture industry will gradually become a reality.