由空间殖民算法生成的用于树木自动修剪策略的多标准决策方法

Gang Zhao, Dian Wang
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引用次数: 0

摘要

果园机械自动化的兴起激发了人们对开发能够自主进行树木修剪作业的机器人的研究兴趣。要实现准确的修剪结果,这些机器人需要强大的感知系统,能够重建三维树木特征并执行适当的修剪策略。三维建模在实现精确修剪结果方面起着至关重要的作用。本文介绍了一种利用空间殖民算法(SCA)为修剪量身定制的专门树建模方法。所提出的方法将 SCA 扩展到三维空间中,生成全面的樱桃树模型。生成的模型以归一化点云数据的形式输出,作为输入数据集。在实际实施过程中,将树种、树的生命周期阶段和修剪策略等各种因素纳入其中,利用多标准决策分析来指导修剪决策。修剪任务被转化为点云神经网络分割任务,确定需要修剪的树干和树枝。这种方法减少了开发过程中的数据采集时间和人力成本。同时,在虚拟环境中进行修剪训练是数字孪生技术的一种应用,使元宇宙与果树自动修剪相结合成为可能。实验结果表明,与其他修剪系统相比,该系统性能优越。总体准确率为 85%,平均准确率和平均联合交叉(IoU)值分别为 0.83 和 0.75。树干和树枝被成功分割,类精确度分别为 0.89 和 0.81,联盟相交 (IoU) 指标分别为 0.79 和 0.72。与使用开源合成树数据集相比,该数据集在相同条件下的总体准确率为 80%,提高了 6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees
The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-dimensional space, generating comprehensive cherry tree models. The resulting models are exported as normalized point cloud data, serving as the input dataset. Multiple criteria decision analysis is utilized to guide pruning decisions, incorporating various factors such as tree species, tree life cycle stages, and pruning strategies during real-world implementation. The pruning task is transformed into a point cloud neural network segmentation task, identifying the trunks and branches to be pruned. This approach reduces the data acquisition time and labor costs during development. Meanwhile, pruning training in a virtual environment is an application of digital twin technology, which makes it possible to combine the meta-universe with the automated pruning of fruit trees. Experimental results demonstrate superior performance compared to other pruning systems. The overall accuracy is 85%, with mean accuracy and mean Intersection over Union (IoU) values of 0.83 and 0.75. Trunks and branches are successfully segmented with class accuracies of 0.89 and 0.81, respectively, and Intersection over Union (IoU) metrics of 0.79 and 0.72. Compared to using the open-source synthetic tree dataset, this dataset yields 80% of the overall accuracy under the same conditions, which is an improvement of 6%.
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