Reproducible Pruning System on Dynamic Natural Plants for Field Agricultural Robots

TL;DR

This paper introduces a multi-modal framework for robotic pruning of dynamic, moving vines in vineyards, combining 3D modeling, force control, and machine learning for improved accuracy and reproducibility.

Contribution

It presents a novel integrated system that enables reproducible pruning skills transfer from simulation to real vineyard environments, handling vine dynamics and heterogeneity.

Findings

Successful simulation and real-world testing of the pruning system.

Effective detection of pruning points using Faster R-CNN and K-means clustering.

Enhanced handling of vine dynamics with Natural Admittance Controller.

Abstract

Pruning is the art of cutting unwanted and unhealthy plant branches and is one of the difficult tasks in the field robotics. It becomes even more complex when the plant branches are moving. Moreover, the reproducibility of robot pruning skills is another challenge to deal with due to the heterogeneous nature of vines in the vineyard. This research proposes a multi-modal framework to deal with the dynamic vines with the aim of sim2real skill transfer. The 3D models of vines are constructed in blender engine and rendered in simulated environment as a need for training the robot. The Natural Admittance Controller (NAC) is applied to deal with the dynamics of vines. It uses force feedback and compensates the friction effects while maintaining the passivity of system. The faster R-CNN is used to detect the spurs on the vines and then statistical pattern recognition algorithm using K-means clustering is applied to find the effective pruning points. The proposed framework is tested in simulated and real environments.