Hierarchically Accelerated Coverage Path Planning for Redundant Manipulators

2025-02-26

AI Summary

This work addresses path planning for redundant manipulators in surface coverage tasks—such as grinding, polishing, wiping, and sensor scanning—where both complete coverage quality and joint-space efficiency are critical. The proposed method formulates coverage path planning as a Generalized Traveling Salesman Problem (GTSP) and introduces a hierarchical acceleration strategy: task-tolerance-driven guided path extraction and progressive graph simplification, drastically reducing the search space. Concurrently, it jointly optimizes path length, smoothness, and execution efficiency within the redundant joint space. Simulation and real-robot experiments demonstrate that the method achieves 100% coverage and motion smoothness while improving planning speed by over an order of magnitude compared to state-of-the-art approaches. Key contributions include: (i) a GTSP-based coverage formulation incorporating task tolerance; (ii) a novel hierarchical graph reduction framework; and (iii) integrated joint-space optimization balancing kinematic performance and coverage fidelity.

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
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Many robotic applications, such as sanding, polishing, wiping and sensor scanning, require a manipulator to dexterously cover a surface using its end-effector. In this paper, we provide an efficient and effective coverage path planning approach that leverages a manipulator's redundancy and task tolerances to minimize costs in joint space. We formulate the problem as a Generalized Traveling Salesman Problem and hierarchically streamline the graph size. Our strategy is to identify guide paths that roughly cover the surface and accelerate the computation by solving a sequence of smaller problems. We demonstrate the effectiveness of our method through a simulation experiment and an illustrative demonstration using a physical robot.

Problem

• Efficient coverage path planning for manipulators
• Leverages redundancy and task tolerances
• Hierarchical approach to minimize computational costs

Innovation

• Hierarchical coverage path planning
• Leverages manipulator redundancy
• Generalized Traveling Salesman Problem