Fig: Overview of the proposed pipeline for active branch manipulation. An initial plant point cloud is
reconstructed from perception and converted into a simulation-ready tetrahedral volumetric mesh. This
deformable model is then used to plan manipulation trajectories that reposition the target such as flowers,
into another robot’s reachable workspace or field of view.
Abstract
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.
Fig: Overview of the proposed pipeline for active branch manipulation. An initial plant point cloud is
reconstructed from perception and converted into a simulation-ready tetrahedral volumetric mesh. This
deformable model is then used to plan manipulation trajectories that reposition the target such as flowers,
into another robot’s reachable workspace or field of view.