Modeling Branches for Active Manipulation using Iterative Parameter Estimation
2026-06-17 • Robotics
Robotics
AI summaryⓘ
The authors developed a way to help robots gently move plant branches by figuring out the branches' physical properties through repeated observations. They create a 3D model of a branch using scanned data and simulate how it bends and moves. By comparing real branch movements with the simulation, they adjust the model to better match reality. This smarter model helps plan robot motions that reduce branch bending, making the robot's movement more careful and effective.
plant branch modelingmaterial parameter estimationfinite element methodtetrahedral meshpoint-cloud datadeformation simulationmotion planningrobotic manipulationagricultural roboticsdeformation energy
Authors
Madhav Rijal, Rashik Shrestha, Trevor Smith, Yu Gu
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.