Constraint Manifold Exploration for Efficient Continuous Coverage Estimation

TL;DR

This paper introduces a sampling-based method to efficiently estimate continuous surface coverage for industrial robot arms, considering tool orientation constraints, enabling better planning in manufacturing tasks.

Contribution

It presents a novel exploration approach in an extended configuration space for assessing surface coverage feasibility, improving over existing trajectory generation methods.

Findings

Accurately estimates surface coverage in complex environments.

Demonstrates efficiency across various robot kinematics.

Validates approach through comprehensive evaluation.

Abstract

Many automated manufacturing processes rely on industrial robot arms to move process-specific tools along workpiece surfaces. In applications like grinding, sanding, spray painting, or inspection, they need to cover a workpiece fully while keeping their tools perpendicular to its surface. While there are approaches to generate trajectories for these applications, there are no sufficient methods for analyzing the feasibility of full surface coverage. This work proposes a sampling-based approach for continuous coverage estimation that explores reachable surface regions in the configuration space. We define an extended ambient configuration space that allows for the representation of tool position and orientation constraints. A continuation-based approach is used to explore it using two different sampling strategies. A thorough evaluation across different kinematics and environments analyzes their runtime and efficiency. This validates our ability to accurately and efficiently calculate surface coverage for complex surfaces in complicated environments.