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

TL;DR

TAPOM introduces a topology-aware planning approach that improves robotic manipulation of elongated objects in cluttered, narrow environments by combining high-level topological analysis with low-level trajectory planning.

Contribution

The paper presents a novel method that explicitly incorporates task-space topological analysis to guide motion planning for complex manipulation tasks involving elongated objects.

Findings

High success rates in low-clearance manipulation tasks

Enhanced planning efficiency over existing methods

Effective handling of narrow passages with elongated objects

Abstract

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.