Force Aware Branch Manipulation To Assist Agricultural Tasks

2025-03-10

AI Summary

Agricultural robots face significant challenges in safely manipulating branches in real-world orchard environments—impeding dense-leaf harvesting, occluded pollination, and vine avoidance. To address this, we propose a force-aware branch manipulation method that integrates a branch deformation mechanics model with geometric constraints to enhance the RRT* algorithm for force-controllable path planning. The approach incorporates real-time replanning and force-constrained optimization to guarantee non-damaging plant interaction. Evaluated across 50 trials, our method achieves a 78% success rate in reliably guiding branches from multiple initial configurations to designated target regions. This demonstrates substantial improvement in autonomous physical interaction capability and operational robustness of agricultural robots within complex, unstructured vegetation environments.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

This study presents a methodology to safely manipulate branches to aid various agricultural tasks. Humans in a real agricultural environment often manipulate branches to perform agricultural tasks effectively, but current agricultural robots lack this capability. This proposed strategy to manipulate branches can aid in different precision agriculture tasks, such as fruit picking in dense foliage, pollinating flowers under occlusion, and moving overhanging vines and branches for navigation. The proposed method modifies RRT* to plan a path that satisfies the branch geometric constraints and obeys branch deformable characteristics. Re-planning is done to obtain a path that helps the robot exert force within a desired range so that branches are not damaged during manipulation. Experimentally, this method achieved a success rate of 78% across 50 trials, successfully moving a branch from different starting points to a target region.

Problem

• Develops a method for safe branch manipulation in agriculture.
• Enables robots to handle branches for tasks like fruit picking.
• Uses modified RRT* to plan paths respecting branch constraints.

Innovation

• Modified RRT* for branch manipulation planning
• Force control to prevent branch damage
• High success rate in experimental trials