PaiP让机器人在柜子内乱堆物体时也能安全移动，靠触觉感知互动
PaiP: An Operational Aware Interactive Planner for Unknown Cabinet Environments
- 用多模态触觉感知推断物体交互特征，实时更新操作代价图
- 在狭窄空间中成功规划路径，避免因视线遮挡导致碰撞
- 适合需要在复杂未知柜体环境里操作的机器人应用
堆叠物体的箱柜场景因视觉遮挡和空间受限，给机器人运动带来巨大挑战。传统无碰撞轨迹规划方法在无可行路径时易失效，甚至因不可见物体引发灾难性碰撞。为此，我们提出一种面向操作的交互式运动规划框架（PaiP），采用实时闭环规划，融合多模态触觉感知。该框架通过感知交互界面的运动效应，自主推断物体交互特征，并将其融入栅格地图生成操作代价图。基于此表征，扩展采样类规划方法，同时优化路径代价与操作代价，实现交互式规划。实验表明，PaiP可在狭小空间中实现鲁棒运动。
原文摘要 · Abstract (English)
Box/cabinet scenarios with stacked objects pose significant challenges for robotic motion due to visual occlusions and constrained free space. Traditional collision-free trajectory planning methods often fail when no collision-free paths exist, and may even lead to catastrophic collisions caused by invisible objects. To overcome these challenges, we propose an operational aware interactive motion planner (PaiP) a real-time closed-loop planning framework utilizing multimodal tactile perception. This framework autonomously infers object interaction features by perceiving motion effects at interaction interfaces. These interaction features are incorporated into grid maps to generate operational cost maps. Building upon this representation, we extend sampling-based planning methods to interactive planning by optimizing both path cost and operational cost. Experimental results demonstrate that PaiP achieves robust motion in narrow spaces.
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