Modeling Branches for Active Manipulation using Iterative Parameter Estimation
It addresses the need for precise branch manipulation in agricultural robotics, offering a practical improvement over existing methods.
The paper presents a method for modeling plant branches via iterative parameter estimation to enable delicate manipulation. In 30 trials, it reduced deformation energy by 35.69% with only an 8.10% increase in path length.
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.