通过迭代参数估计建模植物枝条，实现精准柔性操作。
Modeling Branches for Active Manipulation using Iterative Parameter Estimation
- 基于点云构建四面体模型，用有限元法模拟枝条行为。
- 30次实验中变形能量降低35.69%，路径仅增加8.10%。
- 适合农业机器人进行复杂枝条避障与姿态调整。
本研究提出一种通过迭代参数估计建模多样化植物枝条的方法，以支持精细的枝条操作。在农业机器人中，枝条操作用于植物重新定位、稳定以及清除密集枝叶中的视觉遮挡。该方法从点云数据构建四面体枝条模型，并利用有限元法模拟其力学行为。结合真实观测的形变数据，迭代估计枝条材料参数后，使用形变感知运动规划器计算最优路径，将枝条移动并稳定至另一机器人视野范围内。在具有不同几何结构和材料属性的30组实验中，所提方法平均使变形能量降低35.69%，路径长度增加8.10%。
原文摘要 · Abstract (English)
This study presents a method for modeling diverse plant branches by iteratively estimating material parameters to support delicate branch manipulation. Branch manipulation is necessary in agricultural robotics for plant repositioning, stabilizing, and clearing visual obstructions in dense foliage. The proposed method builds a tetrahedral branch model from point-cloud data and simulates its behavior using the finite element method. Using real observed deformation data, it iteratively estimates branch parameters and then computes an optimal path with a deformation-aware motion planner to move and stabilize branches within another robot's field of view. Across 30 trials on branches with varying geometries and material properties, the proposed method reduced the deformation energy by 35.69% while increasing the path length by 8.10% on average.
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