Task allocation based on single-limb actions for augmented reality-assisted human-robot collaboration

Abstract

Human-robot collaboration (HRC) is a key technology for enabling the human-centric vision of Industry 5.0. Collaborative robots have been deployed on the shop floor to support manual operations and enhance overall productivity. However, poor coordination between robotic systems and human workers may compromise collaborative performance and raise safety risks. This study proposes a new task allocation scheme based on single-limb actions to enhance process efficiency in HRC. Each single-limb task, composed of basic motion elements identified through Therblig analysis, is assigned to either a human or a robotic agent based on individual capabilities and spatial proximity. The scheme is formulated as a mixed-integer programming problem and solved using a Random-Key Genetic Algorithm. The allocation result is validated through a collaborative assembly process by comparing it with a traditional method that does not differentiate between the hands during task assignment. An augmented reality (AR)-assisted tool is developed to support participants in performing their assigned tasks with enhanced situational awareness during an actual experiment. Experimental results indicate that the assembly sequence generated by the proposed scheme leads to a shorter makespan. This study demonstrates that fine-grained planning enables more efficient utilization of human and robotic resources, and highlights the potential of AR to facilitate the practical implementation of complex HRC processes.