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

2025-11-07

AI Summary

Motion planning for slender objects in narrow, confined spaces often fails due to sampling inefficiency and entrapment in local minima. Method: This paper proposes a task-space topology-aware hierarchical motion planning framework. Its core innovation is the first integration of task-space topological analysis into robotic manipulation planning: a high-level topological model identifies critical paths to guide keyframe generation, which in turn directs a low-level sampling-based planner to efficiently explore feasible configuration space. Contribution/Results: The method synergistically couples topological semantic guidance with geometric motion optimization, substantially alleviating the sampling bottleneck in low-clearance scenarios. Experiments demonstrate state-of-the-art success rates across diverse complex narrow-space manipulation tasks, along with significant improvements in planning efficiency—validating its effectiveness and robustness in real-world settings.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Planning robotic manipulation of elongated objects in cluttered narrow spaces
• Overcoming sampling difficulties and local minima in low-clearance scenarios
• Improving success rates and efficiency in constrained environment manipulation

Innovation

• Task-space topology analysis for motion planning
• High-level pathway identification with keyframe generation
• Hierarchical planning combining topological and configuration spaces