Design of Routes for Collaborative Robots in the Automobile Painting Process through a Comparison of Perturbative Heuristics for Iterated Local Search

Abstract

The automotive manufacturing industryfaces diverse challenges, including designing new parts,optimizing schedule times, developing aerodynamic cardesigns, refining painting processes, and advancingautonomous driving. The car painting process is a newoptimization area in the computational context due to thecomplexity and damage caused by various factors. Ourresearch focuses on designing a robotic arm with fivedegrees of freedom that operates in a two-dimensionalplane and is integrated with metaheuristics for pathoptimization. Our methodology consists of defining andlimiting the problem, analyzing requirements, designingthe robotic arm, implementing routes, and conductingtests. For the design of data instances, the Methodologyproposed by [43] was used in this work. Subsequently,a pool of perturbation heuristics and an iterated localsearch algorithm are used, which allowed us to designthe best combination of heuristics that can provide acompetitive solution to the problem of route design forthe robotic arm in the automobile painting process.This study includes a comprehensive review of relatedwork, theoretical concepts, and the application ofmetaheuristics. The results highlight the effectivenessof the proposed heuristics, with the K-OPT heuristicdemonstrating superior performance. Statistical testsconfirm the significance of the differences among theheuristics. This paper concludes with insights into futureresearch directions, emphasizing the importance ofsafety practices and Industry 4.0 technology in mitigatinghealth risks associated with the automotive paintingprocess.