Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In the rapidly advancing field of agricultural robotics, the necessity of training robots in a virtual environment has become essential. Generating training data to automatize the underlying computer vision tasks such as image segmentation, object detection and classification, also heavily relies on such virtual environments as synthetic data is often required to overcome the shortage and lack of variety of real data sets. However, physics engines commonly employed within the robotics community, such as ODE, Simbody, Bullet, and DART, primarily support motion and collision interaction of rigid bodies. This inherent limitation hinders experimentation and progress in handling non-rigid objects such as plants and crops. In this contribution, we present a plugin for the Gazebo simulation platform based on Cosserat rods to model plant motion. It enables the simulation of plants and their interaction with the environment. We demonstrate that, using our plugin, users can conduct harvesting simulations in Gazebo by simulating a robotic arm picking fruits and achieve results comparable to real-world experiments.

翻译：机器人采摘有望提升农业生产力、降低成本、改善食品品质、增强可持续性，并应对劳动力短缺问题。在快速发展的农业机器人领域，虚拟环境中的机器人训练已成为必需品。为自动化图像分割、目标检测与分类等底层计算机视觉任务生成训练数据，同样高度依赖此类虚拟环境——合成数据常被用于克服真实数据集匮乏及多样性不足的问题。然而，机器人社区常用的物理引擎（如ODE、Simbody、Bullet和DART）主要支持刚体的运动与碰撞交互。这一固有缺陷阻碍了处理植物、农作物等非刚性物体时的实验探索与进展。本文提出一种基于Cosserat杆的Gazebo仿真平台插件，用于建模植物运动。该插件支持植物及其与环境交互的仿真。实验表明，用户可通过该插件在Gazebo中模拟机械臂摘取水果的采摘过程，并获得与真实实验高度一致的结果。