Multi objective optimization of human–robot collaboration: A case study in aerospace assembly line

Abstract

Collaborative robotics is becoming increasingly prevalent in industry 5.0, leading to a growing need to improve interactions and collaborations between humans and robots. However, the current approach to defining the sharing of responsibilities between humans and robots is empirical and uses the robot as an active fixture of parts, which is a sub-optimal method for establishing efficient collaboration. This article focuses on optimizing human–robot collaboration on an assembly line within the aerospace industry based on a real-world use case. The methodology adopted in this research entails employing the multi-objective optimization (MOO) method to effectively tackle both the reduction of makespan and the mitigation of working difficulty. Two techniques have been compared for implementation: the weighted sum and the $\varepsilon$-constraint methods, which allow the generation of solutions addressing multiple objectives simultaneously. The results offer chief robotics officers a new tool to design collaboration patterns between humans and robots, with practical implications for real industrial applications. This solution produces several results, including improving company competitiveness and productivity, while maintaining the central role of humans within the company and improving its well-being.