Hierarchically Accelerated Coverage Path Planning for Redundant Manipulators

TL;DR

This paper presents a hierarchical approach to coverage path planning for redundant manipulators, optimizing surface coverage by leveraging redundancy and task tolerances to reduce computational costs.

Contribution

It introduces a novel hierarchical method that formulates the problem as a Generalized Traveling Salesman Problem and accelerates computation by solving smaller subproblems.

Findings

Effective in simulation and physical robot demonstrations.

Reduces computational complexity through hierarchical graph streamlining.

Achieves efficient surface coverage with minimized joint space costs.

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.