TAPOM: Task-Space Topology-Guided Motion Planning for Manipulating Elongated Object in Cluttered Environments

To explore topologically complex free spaces and identify critical pathways, task-space topology analysis is employed to explicitly model free space connectivity and find critical regions. Due to the sampling inefficiency encountered when planning through narrow passages in high-dimensional C-space, a keyframe-guided sampling-based planner is developed that leverages topological insights from high-level analysis to explore C-space. Experimental validation is conducted demonstrating the effectiveness and efficiency of proposed method compared to state-of-the-art planning baselines on manipulation tasks involving elongated objects and narrow passages. Remainder of the article is organized as follows. Section II formally defines the planning problem. Section III details the proposed topology-aware high-level planning approach. In Section IV, the method for low-level path generation is presented. Section V describes experimental setup and results used to evaluate the performance of proposed method. Finally, Section VI provides a brief summary of the work and discusses directions for future research.