机器人在茂密植物丛中靠触觉反馈避障推挤，精准抵达目标不伤枝叶。
RICE: Reactive Interaction Controller for Cluttered Canopy Environment
- 结合位置与触觉反馈，动态权衡绕行与推挤障碍物。
- 35次实验中全部成功抵达目标，无枝条断裂，优于现有无模型控制器。
- 适合农业采摘、修剪等需柔性交互的复杂环境任务。
在如农田冠层这类密集、遮挡严重的环境中，机器人导航面临物理与视觉遮蔽的挑战。传统基于视觉或依赖模型的方法在此类场景下常失效，而安全接触又需避免损伤枝叶。本文提出一种新型反应式控制器，利用末端执行器位置和实时触觉反馈，在接触频繁、结构可变形的环境中实现安全导航。其交互策略基于最小扰动（绕行）与主动推进（穿行）之间的权衡。在三个实验植物场景中，针对被遮挡目标进行了35次测试，该控制器在所有试验中均成功抵达目标，未折断任何枝条，且在鲁棒性和适应性上优于当前最先进的无模型控制器。本工作为复杂接触环境下安全自适应交互奠定了基础，推动未来在植物冠层中的修剪与收获等农业应用。
原文摘要 · Abstract (English)
Robotic navigation in dense, cluttered environments such as agricultural canopies presents significant challenges due to physical and visual occlusion caused by leaves and branches. Traditional vision-based or model-dependent approaches often fail in these settings, where physical interaction without damaging foliage and branches is necessary to reach a target. We present a novel reactive controller that enables safe navigation for a robotic arm in a contact-rich, cluttered, deformable environment using end-effector position and real-time tactile feedback. Our proposed framework's interaction strategy is based on a trade-off between minimizing disturbance by maneuvering around obstacles and pushing through them to move towards the target. We show that over 35 trials in 3 experimental plant setups with an occluded target, the proposed controller successfully reached the target in all trials without breaking any branch and outperformed the state-of-the-art model-free controller in robustness and adaptability. This work lays the foundation for safe, adaptive interaction in cluttered, contact-rich deformable environments, enabling future agricultural tasks such as pruning and harvesting in plant canopies.
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