Constraint Manifold Exploration for Efficient Continuous Coverage Estimation

2026-02-06

AI Summary

Existing methods struggle to efficiently assess the feasibility of industrial robots achieving complete and perpendicular coverage of workpiece surfaces in complex environments. This work proposes a sampling-based continuous coverage estimation approach that, for the first time, integrates constrained manifold exploration with continuous coverage analysis. By constructing an extended configuration space that jointly encodes positional and orientational constraints, the method enables a more comprehensive evaluation of coverage feasibility. Two efficient sampling strategies are specifically designed to navigate this space effectively. The proposed approach significantly improves computational efficiency in assessing full coverage over complex surfaces and demonstrates high accuracy and practical utility across diverse robotic configurations and challenging real-world scenarios.

Technology Category

Intelligent Robots: State EstimationConstraint Satisfaction and Optimization: Mixed Discrete/Continuous OptimizationSearch and Optimization: Sampling/Simulation-based Search

Application Category

Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• surface coverage
• feasibility analysis
• robotic manufacturing
• tool orientation constraint
• configuration space

Innovation

• constraint manifold exploration
• continuous coverage estimation
• configuration space sampling
• tool orientation constraint
• continuation-based planning