Gazebo Plants：基于Cosserat杆的植物-机器人交互仿真
Gazebo Plants: Simulating Plant-Robot Interaction with Cosserat Rods
AI总结：
本文提出一个基于Cosserat杆的Gazebo插件，用于仿真植物运动及植物-机器人交互，支持在虚拟环境中进行机械臂采摘水果的仿真，结果与真实实验相当。
摘要：
机器人采摘有望对农业生产率产生积极影响，降低成本，改善食品质量，增强可持续性，并缓解劳动力短缺问题。在快速发展的农业机器人领域中，在虚拟环境中训练机器人已变得至关重要。生成训练数据以自动化底层计算机视觉任务（如图像分割、目标检测和分类）也严重依赖此类虚拟环境，因为通常需要合成数据来克服真实数据集数量不足和多样性缺乏的问题。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和碰撞交互。这一固有局限性阻碍了在处理植物和农作物等非刚性物体方面的实验和进展。在本贡献中，我们提出了一个基于Cosserat杆的Gazebo仿真平台插件，用于建模植物运动。该插件能够仿真植物及其与环境的交互。我们证明，使用我们的插件，用户可以通过仿真机械臂采摘水果在Gazebo中进行采摘仿真，并获得与真实世界实验相当的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
