通过拓扑分析提升机器人在狭窄空间中操作长物体的规划效率
TAPOM: Task-Space Topology-Guided Motion Planning for Manipulating Elongated Object in Cluttered Environments
- 利用任务空间拓扑分析识别关键路径并生成引导关键帧
- 在低间隙操作任务中成功率显著优于现有方法
- 适合需要精细操控的复杂场景机器人应用
在复杂受限空间中的机器人操作对广泛应用至关重要，但尤其在狭窄通道中操控长物体时极具挑战性。现有规划方法常因采样困难或陷入局部极小值而失效。本文提出拓扑感知物体操作规划（TAPOM），通过高阶任务空间拓扑分析，识别关键路径并生成引导关键帧，用于低层规划器寻找可行配置空间轨迹。实验验证表明，该方法在低间隙操作任务中相比最先进方法展现出更高的成功率和更优效率。该方法为提升机器人在复杂真实环境中的操作能力提供了广泛启示。
原文摘要 · Abstract (English)
Robotic manipulation in complex, constrained spaces is vital for widespread applications but challenging, particularly when navigating narrow passages with elongated objects. Existing planning methods often fail in these low-clearance scenarios due to the sampling difficulties or the local minima. This work proposes Topology-Aware Planning for Object Manipulation (TAPOM), which explicitly incorporates task-space topological analysis to enable efficient planning. TAPOM uses a high-level analysis to identify critical pathways and generate guiding keyframes, which are utilized in a low-level planner to find feasible configuration space trajectories. Experimental validation demonstrates significantly high success rates and improved efficiency over state-of-the-art methods on low-clearance manipulation tasks. This approach offers broad implications for enhancing manipulation capabilities of robots in complex real-world environments.
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