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.

翻译：在复杂受限空间中进行机器人操作对于广泛应用至关重要，但在狭窄通道中操作细长物体尤其具有挑战性。现有规划方法常因采样困难或陷入局部极小值而在这些低间隙场景中失效。本研究提出面向物体操作的拓扑感知规划（TAPOM），该方法显式引入任务空间拓扑分析以实现高效规划。TAPOM通过高层分析识别关键路径并生成引导关键帧，随后在底层规划器中利用这些关键帧寻找可行的构型空间轨迹。实验验证表明，在低间隙操作任务中，该方法相比现有最优方法显著提高了成功率与运行效率。该研究为增强机器人在复杂现实环境中的操作能力提供了广泛启示。