Applying Digital Twin and Multi-Adaptive Genetic Algorithms in Human–Robot Cooperative Assembly Optimization

In this study, we utilized digital twin technology in combination with genetic algorithms to optimize human–robot cooperation a miniature light bulb assembly production line. First, the was used find robot’s motion trajectory; replica of system and human created by combining sensors that track position activity characteristics workspace, which helped prevent conflicts. Then, multi-adaptive algorithm applied calculate optimal ergonomics create worker’s movement schedule. To ensure continuous operation no shortage materials, worker must observe move input conveyor material pallets supply materials system. It aimed provide more for line while allowing task take place parallel robotic operation. The designed reduce number moves required obtain robot always had enough assemble along defined trajectory, thus, saving labor optimizing manufacturing process. optimized path movements performed operator parallel.